dat.raw <- read_dta(paste0(data.raw_path, "Katie_19Jan22.dta"))
colnames(dat.raw)

[1] “familyid” “atwinid” “btwinid” “rorderp5” “torder” “risks”
[7] “cohort” “sampsex” “zygosity” “sethnic” “seswq35” “sisoe5”
[13] “sisoy5” “sisoet5” “sisoyt5” “sisoem5” “sisoym5” “inem5”
[19] “inym5” “hyem5” “hyym5” “inet5” “inyt5” “hyet5”
[25] “hyyt5” “tadhdem5” “tadhdym5” “tadhdet5” “tadhdyt5” “pe2m5”
[31] “pe4m5” “pe7m5” “pe11m5” “pe13m5” “pe25m5” “py2m5”
[37] “py4m5” “py7m5” “py11m5” “py13m5” “py25m5” “trf11e5”
[43] “trf19e5” “trf24e5” “trf30e5” “trf34e5” “trf77e5” “trf11y5”
[49] “trf19y5” “trf24y5” “trf30y5” “trf34y5” “trf77y5” “pe81m5”
[55] “pe82m5” “pe83m5” “pe86m5” “pe87m5” “pe88m5” “pe89m5”
[61] “pe90m5” “pe91m5” “py81m5” “py82m5” “py83m5” “py86m5”
[67] “py87m5” “py88m5” “py89m5” “py90m5” “py91m5” “trf89e5”
[73] “trf90e5” “trf91e5” “trf94e5” “trf95e5” “trf96e5” “trf97e5”
[79] “trf98e5” “trf99e5” “trf89y5” “trf90y5” “trf91y5” “trf94y5”
[85] “trf95y5” “trf96y5” “trf97y5” “trf98y5” “trf99y5” “pe84m5”
[91] “pe85m5” “pe96m5” “pe97m5” “pe92m5” “pe93m5” “pe94m5”
[97] “pe95m5” “pe64m5” “py84m5” “py85m5” “py96m5” “py97m5”
[103] “py92m5” “py93m5” “py94m5” “py95m5” “py64m5” “trf92e5”
[109] “trf93e5” “trf104e5” “trf105e5” “trf100e5” “trf101e5” “trf102e5” [115] “trf103e5” “trf66e5” “trf92y5” “trf93y5” “trf104y5” “trf105y5” [121] “trf100y5” “trf101y5” “trf102y5” “trf103y5” “trf66y5” “sisoe7”
[127] “sisoy7” “sisoet7” “sisoyt7” “sisoem7” “sisoym7” “inem7”
[133] “inym7” “hyem7” “hyym7” “inet7” “inyt7” “hyet7”
[139] “hyyt7” “tadhdem7” “tadhdym7” “tadhdet7” “tadhdyt7” “pe2m7”
[145] “pe4m7” “pe7m7” “pe11m7” “pe13m7” “pe25m7” “py2m7”
[151] “py4m7” “py7m7” “py11m7” “py13m7” “py25m7” “trf11e7”
[157] “trf19e7” “trf24e7” “trf30e7” “trf34e7” “trf77e7” “trf11y7”
[163] “trf19y7” “trf24y7” “trf30y7” “trf34y7” “trf77y7” “pe81m7”
[169] “pe82m7” “pe83m7” “pe86m7” “pe87m7” “pe88m7” “pe89m7”
[175] “pe90m7” “pe91m7” “py81m7” “py82m7” “py83m7” “py86m7”
[181] “py87m7” “py88m7” “py89m7” “py90m7” “py91m7” “trf89e7”
[187] “trf90e7” “trf91e7” “trf94e7” “trf95e7” “trf96e7” “trf97e7”
[193] “trf98e7” “trf99e7” “trf89y7” “trf90y7” “trf91y7” “trf94y7”
[199] “trf95y7” “trf96y7” “trf97y7” “trf98y7” “trf99y7” “pe84m7”
[205] “pe85m7” “pe96m7” “pe97m7” “pe92m7” “pe93m7” “pe94m7”
[211] “pe95m7” “pe64m7” “py84m7” “py85m7” “py96m7” “py97m7”
[217] “py92m7” “py93m7” “py94m7” “py95m7” “py64m7” “trf92e7”
[223] “trf93e7” “trf104e7” “trf105e7” “trf100e7” “trf101e7” “trf102e7” [229] “trf103e7” “trf66e7” “trf92y7” “trf93y7” “trf104y7” “trf105y7” [235] “trf100y7” “trf101y7” “trf102y7” “trf103y7” “trf66y7” “sisoe10”
[241] “sisoy10” “sisoet10” “sisoyt10” “sisoem10” “sisoym10” “inem10”
[247] “inym10” “hyem10” “hyym10” “inet10” “inyt10” “hyet10”
[253] “hyyt10” “tadhdem10” “tadhdym10” “tadhdet10” “tadhdyt10” “pe2m10”
[259] “pe4m10” “pe7m10” “pe11m10” “pe13m10” “pe25m10” “py2m10”
[265] “py4m10” “py7m10” “py11m10” “py13m10” “py25m10” “trf11e10” [271] “trf19e10” “trf24e10” “trf30e10” “trf34e10” “trf77e10” “trf11y10” [277] “trf19y10” “trf24y10” “trf30y10” “trf34y10” “trf77y10” “pe81m10”
[283] “pe82m10” “pe83m10” “pe86m10” “pe87m10” “pe88m10” “pe89m10”
[289] “pe90m10” “pe91m10” “py81m10” “py82m10” “py83m10” “py86m10”
[295] “py87m10” “py88m10” “py89m10” “py90m10” “py91m10” “trf89e10” [301] “trf90e10” “trf91e10” “trf94e10” “trf95e10” “trf96e10” “trf97e10” [307] “trf98e10” “trf99e10” “trf89y10” “trf90y10” “trf91y10” “trf94y10” [313] “trf95y10” “trf96y10” “trf97y10” “trf98y10” “trf99y10” “pe84m10”
[319] “pe85m10” “pe96m10” “pe97m10” “pe92m10” “pe93m10” “pe94m10”
[325] “pe95m10” “pe64m10” “py84m10” “py85m10” “py96m10” “py97m10”
[331] “py92m10” “py93m10” “py94m10” “py95m10” “py64m10” “trf92e10” [337] “trf93e10” “trf104e10” “trf105e10” “trf100e10” “trf101e10” “trf102e10” [343] “trf103e10” “trf66e10” “trf92y10” “trf93y10” “trf104y10” “trf105y10” [349] “trf100y10” “trf101y10” “trf102y10” “trf103y10” “trf66y10” “sisoe12”
[355] “sisoy12” “sisoet12” “sisoyt12” “sisoem12” “sisoym12” “inem12”
[361] “inym12” “hyem12” “hyym12” “inet12” “inyt12” “hyet12”
[367] “hyyt12” “tadhdem12” “tadhdym12” “tadhdet12” “tadhdyt12” “pe2m12”
[373] “pe4m12” “pe7m12” “pe11m12” “pe13m12” “pe25m12” “py2m12”
[379] “py4m12” “py7m12” “py11m12” “py13m12” “py25m12” “trf11e12” [385] “trf19e12” “trf24e12” “trf30e12” “trf34e12” “trf77e12” “trf11y12” [391] “trf19y12” “trf24y12” “trf30y12” “trf34y12” “trf77y12” “pe81m12”
[397] “pe82m12” “pe83m12” “pe86m12” “pe87m12” “pe88m12” “pe89m12”
[403] “pe90m12” “pe91m12” “py81m12” “py82m12” “py83m12” “py86m12”
[409] “py87m12” “py88m12” “py89m12” “py90m12” “py91m12” “trf89e12” [415] “trf90e12” “trf91e12” “trf94e12” “trf95e12” “trf96e12” “trf97e12” [421] “trf98e12” “trf99e12” “trf89y12” “trf90y12” “trf91y12” “trf94y12” [427] “trf95y12” “trf96y12” “trf97y12” “trf98y12” “trf99y12” “pe84m12”
[433] “pe85m12” “pe96m12” “pe97m12” “pe92m12” “pe93m12” “pe94m12”
[439] “pe95m12” “pe64m12” “py84m12” “py85m12” “py96m12” “py97m12”
[445] “py92m12” “py93m12” “py94m12” “py95m12” “py64m12” “trf92e12” [451] “trf93e12” “trf104e12” “trf105e12” “trf100e12” “trf101e12” “trf102e12” [457] “trf103e12” “trf66e12” “trf92y12” “trf93y12” “trf104y12” “trf105y12” [463] “trf100y12” “trf101y12” “trf102y12” “trf103y12” “trf66y12”

dat <- dat.raw %>%
  dplyr::select(id = atwinid,
         pe2m5,   # social isolation mother report raw items (elder variables)
         pe4m5,
         pe7m5,
         pe11m5,
         pe13m5,
         pe25m5,
         trf11e5, # social isolation teacher report raw items (elder variables)
         trf19e5,
         trf24e5,
         trf30e5,
         trf34e5,
         trf77e5,
         pe2m7,   # social isolation mother report raw items (elder variables)
         pe4m7,
         pe7m7,
         pe11m7,
         pe13m7,
         pe25m7,
         trf11e7, # social isolation teacher report raw items (elder variables)
         trf19e7,
         trf24e7,
         trf30e7,
         trf34e7,
         trf77e7,
         pe2m10,   # social isolation mother report raw items (elder variables)
         pe4m10,
         pe7m10,
         pe11m10,
         pe13m10,
         pe25m10,
         trf11e10, # social isolation teacher report raw items (elder variables)
         trf19e10,
         trf24e10,
         trf30e10,
         trf34e10,
         trf77e10,
         pe2m12,   # social isolation mother report raw items (elder variables)
         pe4m12,
         pe7m12,
         pe11m12,
         pe13m12,
         pe25m12,
         trf11e12, # social isolation teacher report raw items (elder variables)
         trf19e12,
         trf24e12,
         trf30e12,
         trf34e12,
         trf77e12,
         pe81m5,  # Inattention mother report raw items (elder variables)
         pe82m5,
         pe83m5,
         pe86m5,
         pe87m5,
         pe88m5,
         pe89m5,
         pe90m5,
         pe91m5,
         pe81m7,  # Inattention mother report raw items (elder variables)
         pe82m7,
         pe83m7,
         pe86m7,
         pe87m7,
         pe88m7,
         pe89m7,
         pe90m7,
         pe91m7,
         pe81m10,  # Inattention mother report raw items (elder variables)
         pe82m10,
         pe83m10,
         pe86m10,
         pe87m10,
         pe88m10,
         pe89m10,
         pe90m10,
         pe91m10,
         pe81m12,  # Inattention mother report raw items (elder variables)
         pe82m12,
         pe83m12,
         pe86m12,
         pe87m12,
         pe88m12,
         pe89m12,
         pe90m12,
         pe91m12,
         trf89e5,  # Inattention teacher report raw items (elder variables)
         trf90e5,
         trf91e5,
         trf94e5,
         trf95e5,
         trf96e5,
         trf97e5,
         trf98e5,
         trf99e5,
         trf89e7,  # Inattention teacher report raw items (elder variables)
         trf90e7,
         trf91e7,
         trf94e7,
         trf95e7,
         trf96e7,
         trf97e7,
         trf98e7,
         trf99e7,
         trf89e10,  # Inattention teacher report raw items (elder variables)
         trf90e10,
         trf91e10,
         trf94e10,
         trf95e10,
         trf96e10,
         trf97e10,
         trf98e10,
         trf99e10,
         trf89e12,  # Inattention teacher report raw items (elder variables)
         trf90e12,
         trf91e12,
         trf94e12,
         trf95e12,
         trf96e12,
         trf97e12,
         trf98e12,
         trf99e12, 
         pe84m5,    # Hyperactivity/impulsivity mother report raw items (elder variables)
         pe85m5,
         pe96m5,
         pe97m5,
         pe92m5,   
         pe93m5, 
         pe94m5, 
         pe95m5,
         pe64m5,
         pe84m7,    # Hyperactivity/impulsivity mother report raw items (elder variables)
         pe85m7,
         pe96m7,
         pe97m7, 
         pe92m7, 
         pe93m7, 
         pe94m7, 
         pe95m7, 
         pe64m7,
         pe84m10,    # Hyperactivity/impulsivity mother report raw items (elder variables)
         pe85m10,
         pe96m10,
         pe97m10, 
         pe92m10, 
         pe93m10, 
         pe94m10, 
         pe95m10, 
         pe64m10,
         pe84m12,    # Hyperactivity/impulsivity mother report raw items (elder variables)
         pe85m12,
         pe96m12,
         pe97m12, 
         pe92m12, 
         pe93m12, 
         pe94m12, 
         pe95m12, 
         pe64m12,
         trf92e5,   # Hyperactivity/impulsivity teacher report raw items (elder variables)
         trf93e5,
         trf104e5,
         trf105e5,
         trf100e5,
         trf101e5,
         trf102e5,
         trf103e5,
         trf66e5,
         trf92e7,   # Hyperactivity/impulsivity teacher report raw items (elder variables)
         trf93e7,
         trf104e7,
         trf105e7,
         trf100e7,
         trf101e7,
         trf102e7,
         trf103e7,
         trf66e7,
         trf92e10,   # Hyperactivity/impulsivity teacher report raw items (elder variables)
         trf93e10,
         trf104e10,
         trf105e10,
         trf100e10,
         trf101e10,
         trf102e10,
         trf103e10,
         trf66e10,
         trf92e12,   # Hyperactivity/impulsivity teacher report raw items (elder variables)
         trf93e12,
         trf104e12,
         trf105e12,
         trf100e12,
         trf101e12,
         trf102e12,
         trf103e12,
         trf66e12  
  )

colnames(dat)

[1] “id” “pe2m5” “pe4m5” “pe7m5” “pe11m5” “pe13m5”
[7] “pe25m5” “trf11e5” “trf19e5” “trf24e5” “trf30e5” “trf34e5”
[13] “trf77e5” “pe2m7” “pe4m7” “pe7m7” “pe11m7” “pe13m7”
[19] “pe25m7” “trf11e7” “trf19e7” “trf24e7” “trf30e7” “trf34e7”
[25] “trf77e7” “pe2m10” “pe4m10” “pe7m10” “pe11m10” “pe13m10”
[31] “pe25m10” “trf11e10” “trf19e10” “trf24e10” “trf30e10” “trf34e10” [37] “trf77e10” “pe2m12” “pe4m12” “pe7m12” “pe11m12” “pe13m12”
[43] “pe25m12” “trf11e12” “trf19e12” “trf24e12” “trf30e12” “trf34e12” [49] “trf77e12” “pe81m5” “pe82m5” “pe83m5” “pe86m5” “pe87m5”
[55] “pe88m5” “pe89m5” “pe90m5” “pe91m5” “pe81m7” “pe82m7”
[61] “pe83m7” “pe86m7” “pe87m7” “pe88m7” “pe89m7” “pe90m7”
[67] “pe91m7” “pe81m10” “pe82m10” “pe83m10” “pe86m10” “pe87m10”
[73] “pe88m10” “pe89m10” “pe90m10” “pe91m10” “pe81m12” “pe82m12”
[79] “pe83m12” “pe86m12” “pe87m12” “pe88m12” “pe89m12” “pe90m12”
[85] “pe91m12” “trf89e5” “trf90e5” “trf91e5” “trf94e5” “trf95e5”
[91] “trf96e5” “trf97e5” “trf98e5” “trf99e5” “trf89e7” “trf90e7”
[97] “trf91e7” “trf94e7” “trf95e7” “trf96e7” “trf97e7” “trf98e7”
[103] “trf99e7” “trf89e10” “trf90e10” “trf91e10” “trf94e10” “trf95e10” [109] “trf96e10” “trf97e10” “trf98e10” “trf99e10” “trf89e12” “trf90e12” [115] “trf91e12” “trf94e12” “trf95e12” “trf96e12” “trf97e12” “trf98e12” [121] “trf99e12” “pe84m5” “pe85m5” “pe96m5” “pe97m5” “pe92m5”
[127] “pe93m5” “pe94m5” “pe95m5” “pe64m5” “pe84m7” “pe85m7”
[133] “pe96m7” “pe97m7” “pe92m7” “pe93m7” “pe94m7” “pe95m7”
[139] “pe64m7” “pe84m10” “pe85m10” “pe96m10” “pe97m10” “pe92m10”
[145] “pe93m10” “pe94m10” “pe95m10” “pe64m10” “pe84m12” “pe85m12”
[151] “pe96m12” “pe97m12” “pe92m12” “pe93m12” “pe94m12” “pe95m12”
[157] “pe64m12” “trf92e5” “trf93e5” “trf104e5” “trf105e5” “trf100e5” [163] “trf101e5” “trf102e5” “trf103e5” “trf66e5” “trf92e7” “trf93e7”
[169] “trf104e7” “trf105e7” “trf100e7” “trf101e7” “trf102e7” “trf103e7” [175] “trf66e7” “trf92e10” “trf93e10” “trf104e10” “trf105e10” “trf100e10” [181] “trf101e10” “trf102e10” “trf103e10” “trf66e10” “trf92e12” “trf93e12” [187] “trf104e12” “trf105e12” “trf100e12” “trf101e12” “trf102e12” “trf103e12” [193] “trf66e12”

# Table of model fit 
table.model.fit <- function(model){
  model.fit <- as.data.frame(t(as.data.frame(model$FIT))) %>%
    dplyr::select(chisq, df, chisq.scaled, cfi, tli, rmsea, rmsea.ci.lower, rmsea.ci.upper, srmr) # not robust because ELSMV estimator
  return(model.fit)
}

# Table of regression and correlation (standardised covariance) coefficients
table.model.coef <- function(model, step){
  if (step == "S1"){
    model.coef <- as.tibble(model$PE[c(121:136),]) %>% dplyr::select(-exo, -std.lv, -std.nox)
    return(model.coef)
  } else if(step == "S2"){
    model.coef <- as.tibble(model$PE[c(121:136),]) %>% dplyr::select(-exo, -label, -std.lv, -std.nox)
    return(model.coef)
  } else if(step == "S3"){
    model.coef <- as.tibble(model$PE[c(121:136),]) %>% dplyr::select(-exo, -label, -std.lv, -std.nox)
    return(model.coef)
  } else {model.coef <- NULL}
}

Cronbach alpha for social isolation items

isolation.mother.age5 <- c("pe2m5", "pe4m5", "pe7m5", "pe11m5", "pe13m5", "pe25m5")
isolation.mother.age7 <- c("pe2m7", "pe4m7", "pe7m7", "pe11m7", "pe13m7", "pe25m7")
isolation.mother.age10 <- c("pe2m10", "pe4m10", "pe7m10", "pe11m10", "pe13m10", "pe25m10")
isolation.mother.age12 <- c("pe2m12", "pe4m12", "pe7m12", "pe11m12", "pe13m12", "pe25m12")

isolation.teacher.age5 <- c( "trf11e5", "trf19e5","trf24e5","trf30e5","trf34e5","trf77e5")
isolation.teacher.age7 <- c( "trf11e7", "trf19e7","trf24e7","trf30e7","trf34e7","trf77e7")
isolation.teacher.age10 <- c( "trf11e10", "trf19e10","trf24e10","trf30e10","trf34e10","trf77e10")
isolation.teacher.age12 <- c( "trf11e12", "trf19e12","trf24e12","trf30e12","trf34e12","trf77e12")

# mother report
cronbach.alpha(dat[isolation.mother.age5], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.mother.age5]’ data-set

Items: 6 Sample units: 2232 alpha: 0.642

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.605 0.675

cronbach.alpha(dat[isolation.mother.age7], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.mother.age7]’ data-set

Items: 6 Sample units: 2232 alpha: 0.721

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.689 0.750

cronbach.alpha(dat[isolation.mother.age10], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.mother.age10]’ data-set

Items: 6 Sample units: 2232 alpha: 0.735

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.703 0.763

cronbach.alpha(dat[isolation.mother.age12], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.mother.age12]’ data-set

Items: 6 Sample units: 2232 alpha: 0.763

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.73 0.79

# teacher report
cronbach.alpha(dat[isolation.teacher.age5], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.teacher.age5]’ data-set

Items: 6 Sample units: 2232 alpha: 0.68

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.635 0.720

cronbach.alpha(dat[isolation.teacher.age7], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.teacher.age7]’ data-set

Items: 6 Sample units: 2232 alpha: 0.734

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.696 0.767

cronbach.alpha(dat[isolation.teacher.age10], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.teacher.age10]’ data-set

Items: 6 Sample units: 2232 alpha: 0.755

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.720 0.788

cronbach.alpha(dat[isolation.teacher.age12], standardized = TRUE, CI = TRUE, na.rm = TRUE)

Standardized Cronbach’s alpha for the ‘dat[isolation.teacher.age12]’ data-set

Items: 6 Sample units: 2232 alpha: 0.766

Bootstrap 95% CI based on 1000 samples 2.5% 97.5% 0.728 0.796

Endorsement rates of each item

isolation.mum.cat.items5 <- c("lonely.report.mum.5",
                     "get.along.report.mum.5",
                     "no.one.loves.report.mum.5",
                     "rather.alone.report.mum.5",
                     "not.liked.report.mum.5",
                     "withdrawn.report.mum.5")

isolation.teach.cat.items5 <- c("lonely.report.teach.5",
                     "get.along.report.teach.5",
                     "no.one.loves.report.teach.5",
                     "rather.alone.report.teach.5",
                     "not.liked.report.teach.5",
                     "withdrawn.report.teach.5")


isolation.mum.teach.cat.items5 <- c("lonely.report.mum.5",
                                    "lonely.report.teach.5",
                     "get.along.report.mum.5",
                     "get.along.report.teach.5",
                     "no.one.loves.report.mum.5",
                     "no.one.loves.report.teach.5",
                     "rather.alone.report.mum.5",
                     "rather.alone.report.teach.5",
                     "not.liked.report.mum.5",
                     "not.liked.report.teach.5",
                     "withdrawn.report.mum.5",
                     "withdrawn.report.teach.5")





isolation.mum.items5 <- c("pe2m5",
                     "pe4m5",
                     "pe7m5",
                     "pe11m5",
                     "pe13m5",
                     "pe25m5")

isolation.teach.items5 <- c("trf11e5",
                     "trf19e5",
                     "trf24e5",
                     "trf30e5",
                     "trf34e5",
                     "trf77e5")




isolation.mum.items7 <- c("lonely.report.7",
                     "get.along.report.7",
                     "no.one.loves.report.7",
                     "rather.alone.report.7",
                     "not.liked.report.7",
                     "withdrawn.report.7")

isolation.mum.items10 <- c("lonely.report.10",
                     "get.along.report.10",
                     "no.one.loves.report.10",
                     "rather.alone.report.10",
                     "not.liked.report.10",
                     "withdrawn.report.10")

dat <- dat %>%
  mutate(lonely.report.teach.5 = 
    if_else(
      trf11e5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(get.along.report.teach.5 = 
    if_else(
      trf19e5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(no.one.loves.report.teach.5 = 
    if_else(
      trf24e5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(rather.alone.report.teach.5 = 
    if_else(
      trf30e5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(not.liked.report.teach.5 = 
    if_else(
      trf34e5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(withdrawn.report.teach.5 = 
    if_else(
      trf77e5 > 0, 
      "Reported",   
      "Not reported"
  ))

dat <- dat %>%
  mutate(lonely.report.mum.5 = 
    if_else(
      pe2m5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(get.along.report.mum.5 = 
    if_else(
      pe4m5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(no.one.loves.report.mum.5 = 
    if_else(
      pe7m5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(rather.alone.report.mum.5 = 
    if_else(
      pe11m5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(not.liked.report.mum.5 = 
    if_else(
      pe13m5 > 0, 
      "Reported",   
      "Not reported"
  )) %>%
  mutate(withdrawn.report.mum.5 = 
    if_else(
      pe25m5 > 0, 
      "Reported",   
      "Not reported"
  ))

endorsement_plot.mum.isolation5 <- sjPlot::plot_likert(
  items = dat[,isolation.mum.cat.items5], 
  title = "Mother endorsement of social isolation items at age 5",
  axis.labels = c("Complains of loneliness",
             "Doesn't get along with other children",
             "Feels no one loves them",
             "Would rather be alone",
             "Not liked by other children",
             "Withdrawn"),
  wrap.labels = 20,
  digits = 0,
  reverse.scale = TRUE,
  cat.neutral = NULL,
  value = "sum.inside",
  catcount = 2,
  geom.colors = c("#b7dee8","#efc00b"))

#have a look at the plot
endorsement_plot.mum.isolation5

endorsement_plot.teach.isolation5 <- sjPlot::plot_likert(
  items = dat[,isolation.teach.cat.items5], 
  title = "Teacher endorsement of social isolation items at age 5",
  axis.labels = c("Complains of loneliness",
             "Doesn't get along with other children",
             "Feels no one loves them",
             "Would rather be alone",
             "Not liked by other children",
             "Withdrawn"),
  wrap.labels = 20,
  digits = 0,
  reverse.scale = TRUE,
  cat.neutral = NULL,
  value = "sum.inside",
  catcount = 2,
  geom.colors = c("#b7dee8","#efc00b"))

#have a look at the plot
endorsement_plot.teach.isolation5

endorsement_plot.mum.teach.isolation5 <- sjPlot::plot_likert(
  items = dat[,isolation.mum.teach.cat.items5], 
  title = "Endorsement of social isolation items at age 5",
  axis.labels = c("Complains of loneliness",
             "Doesn't get along with other children",
             "Feels no one loves them",
             "Would rather be alone",
             "Not liked by other children",
             "Withdrawn"),
  groups = c(1,2,1,2,1,2,1,2,1,2,1,2),
  wrap.labels = 20,
  digits = 0,
  reverse.scale = TRUE,
  cat.neutral = NULL,
  value = "sum.inside",
  catcount = 2,
  geom.colors = c("#b7dee8","#efc00b"))

#have a look at the plot
endorsement_plot.mum.teach.isolation5

# dat <- dat %>%
#   mutate(lonely.report.5 = 
#     case_when(
#       pe2m5 > 0 ~ "Mother reported symptom",
#       trf11e5 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(get.along.report.5 = 
#     case_when(
#       pe4m5 > 0 ~ "Mother reported symptom",
#       trf19e5 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(no.one.loves.report.5 = 
#     case_when(
#       pe7m5 > 0 ~ "Mother reported symptom",
#       trf24e5 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(rather.alone.report.5 = 
#     case_when(
#       pe11m5 > 0 ~ "Mother reported symptom",
#       trf30e5 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(not.liked.report.5 = 
#     case_when(
#       pe13m5 > 0 ~ "Mother reported symptom",
#       trf34e5 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(withdrawn.report.5 = 
#     case_when(
#       pe25m5 > 0 ~ "Mother reported symptom",
#       trf77e5 > 0 ~ "Teacher reported symptom"
#   ))

# dat <- dat %>%
#   mutate(lonely.report.7 = 
#     case_when(
#       pe2m7 > 0 ~ "Mother reported symptom",
#       trf11e7 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(get.along.report.7 = 
#     case_when(
#       pe4m7 > 0 ~ "Mother reported symptom",
#       trf19e7 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(no.one.loves.report.7 = 
#     case_when(
#       pe7m7 > 0 ~ "Mother reported symptom",
#       trf24e7 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(rather.alone.report.7 = 
#     case_when(
#       pe11m7 > 0 ~ "Mother reported symptom",
#       trf30e7 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(not.liked.report.7 = 
#     case_when(
#       pe13m7 > 0 ~ "Mother reported symptom",
#       trf34e7 > 0 ~ "Teacher reported symptom"
#   )) %>%
#   mutate(withdrawn.report.7 = 
#     case_when(
#       pe25m7 > 0 ~ "Mother reported symptom",
#       trf77e7 > 0 ~ "Teacher reported symptom"
#   ))

Load data

The loaded data is for se = robust and listwise deletion models. Need to create a new saved dataset where all models are robust standard errors and pairwise deletion. For now the robust standard errors isn’t computing for the pairwise deletion (I think this is an issue with the package). So all models have se=‘robust’ hashed out

load("../../data_full/data/measurement_models_ordered_WLSMVestimator_8March22.RData")

All RI-CLPM models displayed here

Model	Description
RICLPM_multi_inat_S1	RI-CLPM model using latent factors for mother report ratings for AD and SI, and inattention scores, Step 1
RICLPM_multi_inat_S2	RI-CLPM model using latent factors for mother report ratings for AD and SI, and inattention scores, Step 2
RICLPM_multi_inat_S3	RI-CLPM model using latent factors for mother report ratings for AD and SI, and inattention scores, Step 3
RICLPM_multi_inat_S4	RI-CLPM model using latent factors for mother report ratings for AD and SI, and inattention scores, Step 4
RICLPM_multi_hyp_S1	RI-CLPM model using latent factors for mother report ratings for AD and SI, and hyperactivity scores, Step 1
RICLPM_multi_hyp_S2	RI-CLPM model using latent factors for mother report ratings for AD and SI, and hyperactivity scores, Step 2
RICLPM_multi_hyp_S3	RI-CLPM model using latent factors for mother report ratings for AD and SI, and hyperactivity scores, Step 3
RICLPM_multi_hyp_S4	RI-CLPM model using latent factors for mother report ratings for AD and SI, and hyperactivity scores, Step 4
RICLPM_multi_adhd_S1	RI-CLPM model using latent factors for mother report ratings for AD and SI, and total adhd scores, Step 1
RICLPM_multi_adhd_S2	RI-CLPM model using latent factors for mother report ratings for AD and SI, and total adhd scores, Step 2
RICLPM_multi_adhd_S3	RI-CLPM model using latent factors for mother report ratings for AD and SI, and total adhd scores, Step 3
RICLPM_multi_adhd_S4	RI-CLPM model using latent factors for mother report ratings for AD and SI, and total adhd scores, Step 4
RICLPMt_multi_inat_S1	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and inattention scores, Step 1
RICLPMt_multi_inat_S2	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and inattention scores, Step 2
RICLPMt_multi_inat_S3	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and inattention scores, Step 3
RICLPMt_multi_inat_S4	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and inattention scores, Step 4
RICLPMt_multi_hyp_S1	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and hyperactivity scores, Step 1
RICLPMt_multi_hyp_S2	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and hyperactivity scores, Step 2
RICLPMt_multi_hyp_S3	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and hyperactivity scores, Step 3
RICLPMt_multi_hyp_S4	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and hyperactivity scores, Step 4
RICLPMt_multi_adhd_S1	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and total adhd scores, Step 1
RICLPMt_multi_adhd_S2	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and total adhd scores, Step 2
RICLPMt_multi_adhd_S3	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and total adhd scores, Step 3
RICLPMt_multi_adhd_S4	RI-CLPM model using latent factors for teacher report ratings for AD and SI, and total adhd scores, Step 4

Multiple indicator RI-CLPM

From Mulder and Hamaker (2021): We include multiple indicators for each of the constructs (mother and teacher), while formulating the dynamics over time between the latent variables. There are two ways in which this can be done. First, a random intercept can be included for each indicator and these random intercepts are allowed to be correlated with each other. In addition, a common factor of the multiple indicators is included per occasion to capture the common within-unit variability over time. Second, the random intercepts can be included at the latent level as shown in the bottom panel of (e.g., Seddig, 2020). There is a common factor for each construct at each occasion, which is then being further decomposed into a time-invariant part captured by the random intercept, and a time-varying part that is used to model the within-unit dynamics. These two approaches are nested with the second being a special case of the first.

To allow for a meaningful comparison of factors over time, the factor loadings should be time-invariant, such that there is (at least) weak factorial invariance over time (Meredith, 1993; Millsap, 2011). If we are unable to establish this invariance, it implies that the constructs that we try to measure are interpreted differently over time, and it is difficult to make meaningful comparisons between the constructs measured at different occasions. The below steps need to be considered to establish longitudinal measurement invariance, and detail how the decomposition into within-unit and between-unit variance can be obtained in the context of multiple indicators.

When the ordered = TRUE argument is used, lavaan will automatically switch to the WLSMV estimator: it will use diagonally weighted least squares (DWLS) to estimate the model parameters, but it will use the full weight matrix to compute robust standard errors, and a mean- and variance-adjusted test statistic. The robust CFI, TLI and RMSEA can only be done for MLR estimator, so non-robust fit statistics have been called for all models below as we are using the WLSMV estimator.

Step 1: the configural model (RICLPM_multi_S1)
Step 2: weak factorial invariance (RICLPM_multi_S2)
Step 3: strong factorial invariance (RICLPM_multi_S3)
Step 4: the latent RI-CLPM (RICLPM_multi_S4)

Multiple indicator model from Mulder and Hamaker (2021)

Mother report RI-CLPM: Inattention and social isolation

RICLPM_multi_inat_S1: Inattention step 1

Multiple response items RICLPM mother report inattention ADHD symptoms and social isolation: Step 1, the configural model

The configural model is the least stringent test of invariance, it is designed to test if the constructs have the same pattern of free and fixed loadings. Configural noninvariance means that the pattern of loadings of items on the latent factors differs over the time points. This would then suggest that a slightly different concept is being measured at each time point (Putnick and Bornstein, 2016). To test if the configural variance holds, we will look at the fit of the configural model (S1).

We have six indicators of \(Social isolation\), measured at four waves, we specify six random intercepts to capture the trait-like part of each indicator, that is, RIX1 =~ 1*x11 1*x21 ..., and RIX2 =~ 1*x121 1*x22@1 .... In addition, we specify four within-unit components that capture the state-like part at each wave, using WFX1 =~ x11 x12 x13; WFX2 =~ x21 x22 x23; ....

At the latent within-unit level, we specify the dynamic model using WFX2 ~ WFY1 + WFX1; WFX3 ~ WFY2 + WFX2; .... In addition, we allow the within-person factors at the first wave, and their residuals at subsequent waves to be correlated within each wave, WFX1 ~~ WFY1; WFX2 ~~ WFY2; .... The random intercepts are allowed to be freely correlated with each other through using the cfa() command below.

At the moment all models are using pairwise deletion

RICLPM_multi_inat_S1 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (mother report)
  RIinat1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIinat2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIinat3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIinat4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIinat5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIinat6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIinat7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIinat8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIinat9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves
  WFinat5 =~ pe81m5 + pe82m5 + pe83m5 + pe86m5 + pe87m5 + pe88m5 + pe89m5 + pe90m5 + pe91m5
  WFinat7 =~ pe81m7 + pe82m7 + pe83m7 + pe86m7 + pe87m7 + pe88m7 + pe89m7 + pe90m7 + pe91m7
  WFinat10 =~ pe81m10 + pe82m10 + pe83m10 + pe86m10 + pe87m10 + pe88m10 + pe89m10 + pe90m10 + pe91m10
  WFinat12 =~ pe81m12 + pe82m12 + pe83m12 + pe86m12 + pe87m12 + pe88m12 + pe89m12 + pe90m12 + pe91m12 
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ pe2m5 + pe4m5 + pe7m5 + pe11m5 + pe13m5 + pe25m5 
  WFsi7 =~ pe2m7 + pe4m7 + pe7m7 + pe11m7 + pe13m7 + pe25m7 
  WFsi10 =~ pe2m10 + pe4m10 + pe7m10 + pe11m10 + pe13m10 + pe25m10
  WFsi12 =~ pe2m12 + pe4m12 + pe7m12 + pe11m12 + pe13m12 + pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPM_multi_inat_S1.fit <- cfa(RICLPM_multi_inat_S1, 
                           data = dat,            
                           ordered = TRUE,          # using the "ordered" option will use DWLS with polychoric correlations for the ordinal variables
                           missing = 'pairwise',    # only excludes people who are missing both variables
                           estimator = "WLSMV"      # DWLS to estimate the model parameters using full weight matrix to compute robust se, and mean/variance-adjusted test statistic. 
)

summary(RICLPM_multi_inat_S1.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 178 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316

Number of observations 2232 Number of missing patterns 48

Model Test User Model: Standard Robust Test Statistic 1977.339 2426.651 Degrees of freedom 1574 1574 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.216 Shift parameter 800.375 simple second-order correction

Model Test Baseline Model:

Test statistic 358266.126 88092.703 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.130

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.999 0.990 Tucker-Lewis Index (TLI) 0.999 0.989

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.011 0.016 90 Percent confidence interval - lower 0.009 0.014 90 Percent confidence interval - upper 0.012 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.032 0.032

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
pe81m5 1.000 0.663 0.663 pe81m7 1.000 0.663 0.663 pe81m10 1.000 0.663 0.663 pe81m12 1.000 0.663 0.663 RIinat2 =~
pe82m5 1.000 0.577 0.577 pe82m7 1.000 0.577 0.577 pe82m10 1.000 0.577 0.577 pe82m12 1.000 0.577 0.577 RIinat3 =~
pe83m5 1.000 0.616 0.616 pe83m7 1.000 0.616 0.616 pe83m10 1.000 0.616 0.616 pe83m12 1.000 0.616 0.616 RIinat4 =~
pe86m5 1.000 0.626 0.626 pe86m7 1.000 0.626 0.626 pe86m10 1.000 0.626 0.626 pe86m12 1.000 0.626 0.626 RIinat5 =~
pe87m5 1.000 0.650 0.650 pe87m7 1.000 0.650 0.650 pe87m10 1.000 0.650 0.650 pe87m12 1.000 0.650 0.650 RIinat6 =~
pe88m5 1.000 0.564 0.564 pe88m7 1.000 0.564 0.564 pe88m10 1.000 0.564 0.564 pe88m12 1.000 0.564 0.564 RIinat7 =~
pe89m5 1.000 0.611 0.611 pe89m7 1.000 0.611 0.611 pe89m10 1.000 0.611 0.611 pe89m12 1.000 0.611 0.611 RIinat8 =~
pe90m5 1.000 0.616 0.616 pe90m7 1.000 0.616 0.616 pe90m10 1.000 0.616 0.616 pe90m12 1.000 0.616 0.616 RIinat9 =~
pe91m5 1.000 0.651 0.651 pe91m7 1.000 0.651 0.651 pe91m10 1.000 0.651 0.651 pe91m12 1.000 0.651 0.651 RIsi1 =~
pe2m5 1.000 0.517 0.517 pe2m7 1.000 0.517 0.517 pe2m10 1.000 0.517 0.517 pe2m12 1.000 0.517 0.517 RIsi2 =~
pe4m5 1.000 0.478 0.478 pe4m7 1.000 0.478 0.478 pe4m10 1.000 0.478 0.478 pe4m12 1.000 0.478 0.478 RIsi3 =~
pe7m5 1.000 0.561 0.561 pe7m7 1.000 0.561 0.561 pe7m10 1.000 0.561 0.561 pe7m12 1.000 0.561 0.561 RIsi4 =~
pe11m5 1.000 0.663 0.663 pe11m7 1.000 0.663 0.663 pe11m10 1.000 0.663 0.663 pe11m12 1.000 0.663 0.663 RIsi5 =~
pe13m5 1.000 0.598 0.598 pe13m7 1.000 0.598 0.598 pe13m10 1.000 0.598 0.598 pe13m12 1.000 0.598 0.598 RIsi6 =~
pe25m5 1.000 0.555 0.555 pe25m7 1.000 0.555 0.555 pe25m10 1.000 0.555 0.555 pe25m12 1.000 0.555 0.555 WFinat5 =~
pe81m5 1.000 0.581 0.581 pe82m5 1.057 0.046 23.015 0.000 0.614 0.614 pe83m5 1.058 0.043 24.634 0.000 0.615 0.615 pe86m5 0.886 0.049 18.222 0.000 0.515 0.515 pe87m5 0.620 0.058 10.742 0.000 0.360 0.360 pe88m5 0.965 0.063 15.322 0.000 0.561 0.561 pe89m5 0.946 0.058 16.419 0.000 0.550 0.550 pe90m5 0.834 0.059 14.184 0.000 0.484 0.484 pe91m5 0.784 0.058 13.562 0.000 0.455 0.455 WFinat7 =~
pe81m7 1.000 0.631 0.631 pe82m7 1.023 0.043 23.875 0.000 0.646 0.646 pe83m7 1.016 0.040 25.491 0.000 0.641 0.641 pe86m7 0.884 0.045 19.734 0.000 0.558 0.558 pe87m7 0.642 0.054 11.939 0.000 0.405 0.405 pe88m7 0.878 0.053 16.419 0.000 0.554 0.554 pe89m7 0.842 0.051 16.592 0.000 0.532 0.532 pe90m7 0.800 0.055 14.524 0.000 0.505 0.505 pe91m7 0.943 0.052 18.226 0.000 0.595 0.595 WFinat10 =~
pe81m10 1.000 0.638 0.638 pe82m10 1.018 0.038 26.783 0.000 0.649 0.649 pe83m10 1.019 0.035 29.205 0.000 0.650 0.650 pe86m10 0.835 0.042 19.925 0.000 0.533 0.533 pe87m10 0.709 0.048 14.924 0.000 0.452 0.452 pe88m10 0.882 0.048 18.548 0.000 0.562 0.562 pe89m10 0.822 0.048 17.207 0.000 0.524 0.524 pe90m10 0.715 0.051 14.013 0.000 0.456 0.456 pe91m10 0.812 0.050 16.393 0.000 0.518 0.518 WFinat12 =~
pe81m12 1.000 0.665 0.665 pe82m12 1.077 0.032 33.796 0.000 0.716 0.716 pe83m12 1.006 0.029 34.593 0.000 0.669 0.669 pe86m12 0.902 0.035 25.950 0.000 0.600 0.600 pe87m12 0.786 0.044 18.063 0.000 0.523 0.523 pe88m12 0.877 0.041 21.545 0.000 0.583 0.583 pe89m12 0.816 0.041 20.108 0.000 0.543 0.543 pe90m12 0.805 0.043 18.694 0.000 0.535 0.535 pe91m12 0.849 0.042 19.997 0.000 0.565 0.565 WFsi5 =~
pe2m5 1.000 0.471 0.471 pe4m5 1.594 0.233 6.833 0.000 0.751 0.751 pe7m5 0.771 0.124 6.241 0.000 0.363 0.363 pe11m5 0.992 0.160 6.188 0.000 0.467 0.467 pe13m5 1.335 0.203 6.578 0.000 0.629 0.629 pe25m5 1.405 0.205 6.860 0.000 0.662 0.662 WFsi7 =~
pe2m7 1.000 0.535 0.535 pe4m7 1.469 0.160 9.187 0.000 0.786 0.786 pe7m7 0.961 0.124 7.729 0.000 0.514 0.514 pe11m7 0.911 0.118 7.715 0.000 0.487 0.487 pe13m7 1.341 0.152 8.849 0.000 0.717 0.717 pe25m7 1.292 0.153 8.429 0.000 0.691 0.691 WFsi10 =~
pe2m10 1.000 0.586 0.586 pe4m10 1.252 0.114 10.940 0.000 0.734 0.734 pe7m10 0.927 0.101 9.209 0.000 0.543 0.543 pe11m10 0.737 0.092 8.039 0.000 0.432 0.432 pe13m10 1.239 0.120 10.324 0.000 0.726 0.726 pe25m10 1.168 0.120 9.695 0.000 0.685 0.685 WFsi12 =~
pe2m12 1.000 0.689 0.689 pe4m12 1.081 0.081 13.392 0.000 0.745 0.745 pe7m12 0.941 0.075 12.486 0.000 0.649 0.649 pe11m12 0.774 0.072 10.718 0.000 0.534 0.534 pe13m12 1.024 0.085 12.099 0.000 0.706 0.706 pe25m12 1.008 0.086 11.688 0.000 0.695 0.695

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.534 0.058 9.212 0.000 0.491 0.491 WFsi5 0.081 0.079 1.019 0.308 0.060 0.060 WFsi7 ~
WFinat5 0.081 0.060 1.341 0.180 0.088 0.088 WFsi5 0.664 0.118 5.627 0.000 0.585 0.585 WFinat10 ~
WFinat7 0.445 0.055 8.155 0.000 0.441 0.441 WFsi7 0.157 0.079 1.997 0.046 0.132 0.132 WFsi10 ~
WFinat7 0.164 0.055 2.967 0.003 0.177 0.177 WFsi7 0.602 0.094 6.414 0.000 0.549 0.549 WFinat12 ~
WFinat10 0.739 0.035 21.058 0.000 0.709 0.709 WFsi10 0.073 0.050 1.474 0.141 0.065 0.065 WFsi12 ~
WFinat10 0.161 0.047 3.396 0.001 0.149 0.149 WFsi10 0.852 0.074 11.571 0.000 0.725 0.725

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.123 0.037 3.341 0.001 0.450 0.450 .WFinat7 ~~
.WFsi7 0.073 0.014 5.052 0.000 0.325 0.325 .WFinat10 ~~
.WFsi10 0.063 0.014 4.594 0.000 0.255 0.255 .WFinat12 ~~
.WFsi12 0.064 0.011 5.554 0.000 0.344 0.344 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.360 0.037 9.735 0.000 0.940 0.940 RIinat3 0.416 0.036 11.695 0.000 1.016 1.016 RIinat4 0.321 0.032 9.981 0.000 0.772 0.772 RIinat5 0.205 0.028 7.249 0.000 0.476 0.476 RIinat6 0.191 0.033 5.849 0.000 0.510 0.510 RIinat7 0.312 0.032 9.861 0.000 0.769 0.769 RIinat8 0.102 0.031 3.331 0.001 0.249 0.249 RIinat9 0.258 0.033 7.921 0.000 0.597 0.597 RIsi1 0.066 0.038 1.735 0.083 0.191 0.191 RIsi2 0.140 0.045 3.141 0.002 0.443 0.443 RIsi3 0.074 0.034 2.168 0.030 0.199 0.199 RIsi4 0.031 0.031 0.970 0.332 0.069 0.069 RIsi5 0.185 0.043 4.260 0.000 0.467 0.467 RIsi6 0.149 0.042 3.559 0.000 0.404 0.404 RIinat2 ~~
RIinat3 0.325 0.037 8.683 0.000 0.914 0.914 RIinat4 0.294 0.034 8.708 0.000 0.813 0.813 RIinat5 0.161 0.029 5.539 0.000 0.430 0.430 RIinat6 0.183 0.034 5.425 0.000 0.563 0.563 RIinat7 0.282 0.033 8.544 0.000 0.800 0.800 RIinat8 0.114 0.031 3.664 0.000 0.322 0.322 RIinat9 0.222 0.034 6.585 0.000 0.590 0.590 RIsi1 0.031 0.039 0.792 0.428 0.103 0.103 RIsi2 0.095 0.047 2.026 0.043 0.344 0.344 RIsi3 0.050 0.035 1.429 0.153 0.156 0.156 RIsi4 0.009 0.033 0.271 0.787 0.023 0.023 RIsi5 0.126 0.046 2.772 0.006 0.366 0.366 RIsi6 0.105 0.044 2.393 0.017 0.329 0.329 RIinat3 ~~
RIinat4 0.303 0.033 9.275 0.000 0.785 0.785 RIinat5 0.191 0.028 6.751 0.000 0.476 0.476 RIinat6 0.176 0.033 5.356 0.000 0.505 0.505 RIinat7 0.268 0.032 8.383 0.000 0.713 0.713 RIinat8 0.110 0.031 3.567 0.000 0.289 0.289 RIinat9 0.241 0.033 7.370 0.000 0.601 0.601 RIsi1 0.049 0.038 1.281 0.200 0.152 0.152 RIsi2 0.106 0.045 2.353 0.019 0.359 0.359 RIsi3 0.066 0.034 1.914 0.056 0.190 0.190 RIsi4 0.003 0.032 0.080 0.936 0.006 0.006 RIsi5 0.155 0.044 3.519 0.000 0.420 0.420 RIsi6 0.119 0.042 2.817 0.005 0.347 0.347 RIinat4 ~~
RIinat5 0.229 0.026 8.910 0.000 0.563 0.563 RIinat6 0.238 0.030 7.977 0.000 0.674 0.674 RIinat7 0.304 0.029 10.444 0.000 0.796 0.796 RIinat8 0.161 0.028 5.753 0.000 0.418 0.418 RIinat9 0.255 0.030 8.548 0.000 0.625 0.625 RIsi1 0.045 0.034 1.332 0.183 0.138 0.138 RIsi2 0.134 0.040 3.339 0.001 0.448 0.448 RIsi3 0.084 0.031 2.718 0.007 0.238 0.238 RIsi4 0.009 0.029 0.329 0.742 0.023 0.023 RIsi5 0.177 0.040 4.466 0.000 0.474 0.474 RIsi6 0.121 0.037 3.220 0.001 0.347 0.347 RIinat5 ~~
RIinat6 0.220 0.026 8.449 0.000 0.601 0.601 RIinat7 0.195 0.026 7.570 0.000 0.491 0.491 RIinat8 0.157 0.025 6.285 0.000 0.392 0.392 RIinat9 0.285 0.027 10.714 0.000 0.674 0.674 RIsi1 0.123 0.030 4.115 0.000 0.367 0.367 RIsi2 0.182 0.034 5.318 0.000 0.585 0.585 RIsi3 0.084 0.027 3.119 0.002 0.229 0.229 RIsi4 0.150 0.026 5.752 0.000 0.347 0.347 RIsi5 0.229 0.035 6.581 0.000 0.591 0.591 RIsi6 0.206 0.033 6.175 0.000 0.570 0.570 RIinat6 ~~
RIinat7 0.207 0.029 7.039 0.000 0.602 0.602 RIinat8 0.270 0.029 9.446 0.000 0.777 0.777 RIinat9 0.252 0.030 8.280 0.000 0.685 0.685 RIsi1 0.011 0.035 0.323 0.747 0.039 0.039 RIsi2 0.108 0.041 2.649 0.008 0.401 0.401 RIsi3 0.044 0.032 1.392 0.164 0.139 0.139 RIsi4 0.016 0.029 0.556 0.578 0.043 0.043 RIsi5 0.141 0.041 3.481 0.000 0.420 0.420 RIsi6 0.092 0.039 2.379 0.017 0.295 0.295 RIinat7 ~~
RIinat8 0.115 0.027 4.187 0.000 0.305 0.305 RIinat9 0.223 0.030 7.512 0.000 0.561 0.561 RIsi1 0.084 0.034 2.487 0.013 0.267 0.267 RIsi2 0.144 0.040 3.634 0.000 0.495 0.495 RIsi3 0.088 0.031 2.871 0.004 0.258 0.258 RIsi4 0.076 0.028 2.680 0.007 0.188 0.188 RIsi5 0.179 0.040 4.513 0.000 0.490 0.490 RIsi6 0.197 0.037 5.254 0.000 0.580 0.580 RIinat8 ~~
RIinat9 0.265 0.029 9.218 0.000 0.661 0.661 RIsi1 0.056 0.033 1.699 0.089 0.177 0.177 RIsi2 0.109 0.038 2.843 0.004 0.369 0.369 RIsi3 0.057 0.030 1.921 0.055 0.166 0.166 RIsi4 0.009 0.027 0.340 0.734 0.023 0.023 RIsi5 0.131 0.039 3.366 0.001 0.355 0.355 RIsi6 0.032 0.037 0.865 0.387 0.092 0.092 RIinat9 ~~
RIsi1 0.101 0.036 2.822 0.005 0.301 0.301 RIsi2 0.133 0.041 3.232 0.001 0.426 0.426 RIsi3 0.079 0.032 2.473 0.013 0.215 0.215 RIsi4 0.086 0.029 2.933 0.003 0.200 0.200 RIsi5 0.167 0.042 3.969 0.000 0.430 0.430 RIsi6 0.176 0.039 4.492 0.000 0.488 0.488 RIsi1 ~~
RIsi2 0.072 0.063 1.147 0.251 0.293 0.293 RIsi3 0.181 0.048 3.732 0.000 0.623 0.623 RIsi4 0.074 0.045 1.667 0.096 0.216 0.216 RIsi5 0.103 0.064 1.603 0.109 0.332 0.332 RIsi6 0.113 0.061 1.869 0.062 0.394 0.394 RIsi2 ~~
RIsi3 0.043 0.056 0.779 0.436 0.162 0.162 RIsi4 0.120 0.050 2.414 0.016 0.380 0.380 RIsi5 0.280 0.073 3.838 0.000 0.979 0.979 RIsi6 0.133 0.069 1.930 0.054 0.502 0.502 RIsi3 ~~
RIsi4 -0.040 0.039 -1.029 0.303 -0.107 -0.107 RIsi5 0.069 0.057 1.207 0.227 0.205 0.205 RIsi6 0.023 0.053 0.423 0.672 0.072 0.072 RIsi4 ~~
RIsi5 0.108 0.050 2.146 0.032 0.272 0.272 RIsi6 0.291 0.049 5.883 0.000 0.789 0.789 RIsi5 ~~
RIsi6 0.156 0.069 2.275 0.023 0.472 0.472

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.223 0.223 0.223 .pe81m7 0.162 0.162 0.162 .pe81m10 0.153 0.153 0.153 .pe81m12 0.118 0.118 0.118 .pe82m5 0.290 0.290 0.290 .pe82m7 0.250 0.250 0.250 .pe82m10 0.246 0.246 0.246 .pe82m12 0.154 0.154 0.154 .pe83m5 0.242 0.242 0.242 .pe83m7 0.209 0.209 0.209 .pe83m10 0.198 0.198 0.198 .pe83m12 0.172 0.172 0.172 .pe86m5 0.343 0.343 0.343 .pe86m7 0.297 0.297 0.297 .pe86m10 0.324 0.324 0.324 .pe86m12 0.248 0.248 0.248 .pe87m5 0.448 0.448 0.448 .pe87m7 0.414 0.414 0.414 .pe87m10 0.374 0.374 0.374 .pe87m12 0.304 0.304 0.304 .pe88m5 0.368 0.368 0.368 .pe88m7 0.375 0.375 0.375 .pe88m10 0.366 0.366 0.366 .pe88m12 0.342 0.342 0.342 .pe89m5 0.325 0.325 0.325 .pe89m7 0.344 0.344 0.344 .pe89m10 0.353 0.353 0.353 .pe89m12 0.332 0.332 0.332 .pe90m5 0.386 0.386 0.386 .pe90m7 0.365 0.365 0.365 .pe90m10 0.412 0.412 0.412 .pe90m12 0.334 0.334 0.334 .pe91m5 0.369 0.369 0.369 .pe91m7 0.222 0.222 0.222 .pe91m10 0.308 0.308 0.308 .pe91m12 0.257 0.257 0.257 .pe2m5 0.511 0.511 0.511 .pe2m7 0.446 0.446 0.446 .pe2m10 0.388 0.388 0.388 .pe2m12 0.258 0.258 0.258 .pe4m5 0.208 0.208 0.208 .pe4m7 0.153 0.153 0.153 .pe4m10 0.233 0.233 0.233 .pe4m12 0.217 0.217 0.217 .pe7m5 0.553 0.553 0.553 .pe7m7 0.421 0.421 0.421 .pe7m10 0.390 0.390 0.390 .pe7m12 0.264 0.264 0.264 .pe11m5 0.341 0.341 0.341 .pe11m7 0.322 0.322 0.322 .pe11m10 0.373 0.373 0.373 .pe11m12 0.275 0.275 0.275 .pe13m5 0.247 0.247 0.247 .pe13m7 0.128 0.128 0.128 .pe13m10 0.115 0.115 0.115 .pe13m12 0.145 0.145 0.145 .pe25m5 0.254 0.254 0.254 .pe25m7 0.214 0.214 0.214 .pe25m10 0.223 0.223 0.223 .pe25m12 0.209 0.209 0.209 RIinat1 0.440 0.036 12.201 0.000 1.000 1.000 RIinat2 0.333 0.040 8.359 0.000 1.000 1.000 RIinat3 0.380 0.037 10.171 0.000 1.000 1.000 RIinat4 0.392 0.031 12.832 0.000 1.000 1.000 RIinat5 0.422 0.024 17.702 0.000 1.000 1.000 RIinat6 0.318 0.032 9.821 0.000 1.000 1.000 RIinat7 0.373 0.031 12.053 0.000 1.000 1.000 RIinat8 0.380 0.028 13.709 0.000 1.000 1.000 RIinat9 0.424 0.032 13.242 0.000 1.000 1.000 RIsi1 0.268 0.059 4.573 0.000 1.000 1.000 RIsi2 0.228 0.077 2.979 0.003 1.000 1.000 RIsi3 0.315 0.046 6.901 0.000 1.000 1.000 RIsi4 0.440 0.039 11.237 0.000 1.000 1.000 RIsi5 0.357 0.074 4.829 0.000 1.000 1.000 RIsi6 0.308 0.071 4.368 0.000 1.000 1.000 WFinat5 0.337 0.039 8.751 0.000 1.000 1.000 .WFinat7 0.290 0.020 14.684 0.000 0.728 0.728 .WFinat10 0.300 0.019 15.668 0.000 0.739 0.739 .WFinat12 0.201 0.014 14.257 0.000 0.454 0.454 WFsi5 0.222 0.076 2.935 0.003 1.000 1.000 .WFsi7 0.173 0.036 4.825 0.000 0.604 0.604 .WFsi10 0.201 0.033 6.165 0.000 0.585 0.585 .WFsi12 0.170 0.025 6.810 0.000 0.359 0.359

For the listwise deletion model: S1 Model fit (robust indices): Comparative Fit Index (CFI) 0.990 (>0.95) Tucker-Lewis Index (TLI) 0.998 (>0.95)
RMSEA 0.016 (≤ 0.06)
90 Percent confidence interval - lower 0.015 90 Percent confidence interval - upper 0.017
SRMR 0.033 (≤ 0.08)

We can conclude that the model shows very good fit.

RICLPM_multi_inat_S2: Inattention step 2

Multiple response items RICLPM mother report inattention ADHD symptoms and social isolation: Step 2. If configural variance is supported, we next test for weak invariance (sometimes called metric invariance). This tests for the equivalence of the item loadings on the factors. Weak (metric) invariance means that each item contributes to the latent construct to a similar degree across groups. this is tested by constraining factor loadings (i.e., the loadings of the items on the constructs) to be equivalent in the two time points (Putnick and Bornstein, 2016). The model with constrained factor loadings (S2) is then compared to the configural invariance model (S1) to determine fit. If the overall model fit is significantly worse in the weak invariance model compared to the configural invariance model, it indicates that at least one loading is not equivalent across the groups, and weak invariance is not supported.

In our second step model, we constrain the factor loadings to be invariant over time using the labels a*, b*, c*, d* etc, in the “within” part of the model.

RICLPM_multi_inat_S2 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (mother report)
  RIinat1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIinat2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIinat3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIinat4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIinat5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIinat6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIinat7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIinat8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIinat9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  WFinat5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5
  WFinat7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7
  WFinat10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10
  WFinat12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  WFsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  WFsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  WFsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPM_multi_inat_S2.fit <- cfa(RICLPM_multi_inat_S2, 
                           data = dat, 
                           estimator = "WLSMV", 
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPM_multi_inat_S2.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 146 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316 Number of equality constraints 39

Number of observations 2232 Number of missing patterns 48

Model Test User Model: Standard Robust Test Statistic 2267.886 2385.499 Degrees of freedom 1613 1613 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.521 Shift parameter 894.840 simple second-order correction

Model Test Baseline Model:

Test statistic 358266.126 88092.703 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.130

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.998 0.991 Tucker-Lewis Index (TLI) 0.998 0.990

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.013 0.015 90 Percent confidence interval - lower 0.012 0.013 90 Percent confidence interval - upper 0.015 0.016 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.035 0.035

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
pe81m5 1.000 0.692 0.692 pe81m7 1.000 0.692 0.692 pe81m10 1.000 0.692 0.692 pe81m12 1.000 0.692 0.692 RIinat2 =~
pe82m5 1.000 0.610 0.610 pe82m7 1.000 0.610 0.610 pe82m10 1.000 0.610 0.610 pe82m12 1.000 0.610 0.610 RIinat3 =~
pe83m5 1.000 0.651 0.651 pe83m7 1.000 0.651 0.651 pe83m10 1.000 0.651 0.651 pe83m12 1.000 0.651 0.651 RIinat4 =~
pe86m5 1.000 0.648 0.648 pe86m7 1.000 0.648 0.648 pe86m10 1.000 0.648 0.648 pe86m12 1.000 0.648 0.648 RIinat5 =~
pe87m5 1.000 0.660 0.660 pe87m7 1.000 0.660 0.660 pe87m10 1.000 0.660 0.660 pe87m12 1.000 0.660 0.660 RIinat6 =~
pe88m5 1.000 0.592 0.592 pe88m7 1.000 0.592 0.592 pe88m10 1.000 0.592 0.592 pe88m12 1.000 0.592 0.592 RIinat7 =~
pe89m5 1.000 0.637 0.637 pe89m7 1.000 0.637 0.637 pe89m10 1.000 0.637 0.637 pe89m12 1.000 0.637 0.637 RIinat8 =~
pe90m5 1.000 0.635 0.635 pe90m7 1.000 0.635 0.635 pe90m10 1.000 0.635 0.635 pe90m12 1.000 0.635 0.635 RIinat9 =~
pe91m5 1.000 0.671 0.671 pe91m7 1.000 0.671 0.671 pe91m10 1.000 0.671 0.671 pe91m12 1.000 0.671 0.671 RIsi1 =~
pe2m5 1.000 0.491 0.491 pe2m7 1.000 0.491 0.491 pe2m10 1.000 0.491 0.491 pe2m12 1.000 0.491 0.491 RIsi2 =~
pe4m5 1.000 0.559 0.559 pe4m7 1.000 0.559 0.559 pe4m10 1.000 0.559 0.559 pe4m12 1.000 0.559 0.559 RIsi3 =~
pe7m5 1.000 0.524 0.524 pe7m7 1.000 0.524 0.524 pe7m10 1.000 0.524 0.524 pe7m12 1.000 0.524 0.524 RIsi4 =~
pe11m5 1.000 0.681 0.681 pe11m7 1.000 0.681 0.681 pe11m10 1.000 0.681 0.681 pe11m12 1.000 0.681 0.681 RIsi5 =~
pe13m5 1.000 0.636 0.636 pe13m7 1.000 0.636 0.636 pe13m10 1.000 0.636 0.636 pe13m12 1.000 0.636 0.636 RIsi6 =~
pe25m5 1.000 0.604 0.604 pe25m7 1.000 0.604 0.604 pe25m10 1.000 0.604 0.604 pe25m12 1.000 0.604 0.604 WFinat5 =~
pe81m5 (a) 1.000 0.560 0.560 pe82m5 (b) 1.054 0.029 35.865 0.000 0.590 0.590 pe83m5 (c) 1.012 0.027 37.368 0.000 0.566 0.566 pe86m5 (d) 0.880 0.032 27.904 0.000 0.492 0.492 pe87m5 (e) 0.733 0.036 20.340 0.000 0.410 0.410 pe88m5 (f) 0.885 0.036 24.310 0.000 0.495 0.495 pe89m5 (g) 0.826 0.035 23.652 0.000 0.462 0.462 pe90m5 (h) 0.783 0.037 20.944 0.000 0.438 0.438 pe91m5 (i) 0.857 0.037 22.931 0.000 0.480 0.480 WFinat7 =~
pe81m7 (a) 1.000 0.602 0.602 pe82m7 (b) 1.054 0.029 35.865 0.000 0.634 0.634 pe83m7 (c) 1.012 0.027 37.368 0.000 0.609 0.609 pe86m7 (d) 0.880 0.032 27.904 0.000 0.529 0.529 pe87m7 (e) 0.733 0.036 20.340 0.000 0.441 0.441 pe88m7 (f) 0.885 0.036 24.310 0.000 0.532 0.532 pe89m7 (g) 0.826 0.035 23.652 0.000 0.497 0.497 pe90m7 (h) 0.783 0.037 20.944 0.000 0.471 0.471 pe91m7 (i) 0.857 0.037 22.931 0.000 0.516 0.516 WFinat10 =~
pe81m10 (a) 1.000 0.593 0.593 pe82m10 (b) 1.054 0.029 35.865 0.000 0.625 0.625 pe83m10 (c) 1.012 0.027 37.368 0.000 0.600 0.600 pe86m10 (d) 0.880 0.032 27.904 0.000 0.522 0.522 pe87m10 (e) 0.733 0.036 20.340 0.000 0.435 0.435 pe88m10 (f) 0.885 0.036 24.310 0.000 0.525 0.525 pe89m10 (g) 0.826 0.035 23.652 0.000 0.490 0.490 pe90m10 (h) 0.783 0.037 20.944 0.000 0.464 0.464 pe91m10 (i) 0.857 0.037 22.931 0.000 0.508 0.508 WFinat12 =~
pe81m12 (a) 1.000 0.642 0.642 pe82m12 (b) 1.054 0.029 35.865 0.000 0.676 0.676 pe83m12 (c) 1.012 0.027 37.368 0.000 0.649 0.649 pe86m12 (d) 0.880 0.032 27.904 0.000 0.564 0.564 pe87m12 (e) 0.733 0.036 20.340 0.000 0.470 0.470 pe88m12 (f) 0.885 0.036 24.310 0.000 0.568 0.568 pe89m12 (g) 0.826 0.035 23.652 0.000 0.530 0.530 pe90m12 (h) 0.783 0.037 20.944 0.000 0.502 0.502 pe91m12 (i) 0.857 0.037 22.931 0.000 0.550 0.550 WFsi5 =~
pe2m5 (j) 1.000 0.579 0.579 pe4m5 (k) 1.050 0.081 12.891 0.000 0.607 0.607 pe7m5 (l) 0.958 0.079 12.078 0.000 0.554 0.554 pe11m5 (m) 0.701 0.069 10.100 0.000 0.406 0.406 pe13m5 (n) 1.037 0.092 11.270 0.000 0.600 0.600 pe25m5 (o) 0.985 0.086 11.441 0.000 0.570 0.570 WFsi7 =~
pe2m7 (j) 1.000 0.648 0.648 pe4m7 (k) 1.050 0.081 12.891 0.000 0.680 0.680 pe7m7 (l) 0.958 0.079 12.078 0.000 0.621 0.621 pe11m7 (m) 0.701 0.069 10.100 0.000 0.454 0.454 pe13m7 (n) 1.037 0.092 11.270 0.000 0.672 0.672 pe25m7 (o) 0.985 0.086 11.441 0.000 0.638 0.638 WFsi10 =~
pe2m10 (j) 1.000 0.640 0.640 pe4m10 (k) 1.050 0.081 12.891 0.000 0.672 0.672 pe7m10 (l) 0.958 0.079 12.078 0.000 0.613 0.613 pe11m10 (m) 0.701 0.069 10.100 0.000 0.449 0.449 pe13m10 (n) 1.037 0.092 11.270 0.000 0.663 0.663 pe25m10 (o) 0.985 0.086 11.441 0.000 0.630 0.630 WFsi12 =~
pe2m12 (j) 1.000 0.677 0.677 pe4m12 (k) 1.050 0.081 12.891 0.000 0.711 0.711 pe7m12 (l) 0.958 0.079 12.078 0.000 0.648 0.648 pe11m12 (m) 0.701 0.069 10.100 0.000 0.475 0.475 pe13m12 (n) 1.037 0.092 11.270 0.000 0.702 0.702 pe25m12 (o) 0.985 0.086 11.441 0.000 0.667 0.667

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.504 0.053 9.447 0.000 0.469 0.469 WFsi5 -0.103 0.066 -1.566 0.117 -0.099 -0.099 WFsi7 ~
WFinat5 -0.044 0.068 -0.655 0.513 -0.038 -0.038 WFsi5 0.669 0.075 8.980 0.000 0.598 0.598 WFinat10 ~
WFinat7 0.441 0.053 8.312 0.000 0.447 0.447 WFsi7 -0.032 0.064 -0.509 0.610 -0.035 -0.035 WFsi10 ~
WFinat7 0.040 0.060 0.667 0.505 0.038 0.038 WFsi7 0.579 0.074 7.835 0.000 0.587 0.587 WFinat12 ~
WFinat10 0.777 0.031 25.204 0.000 0.718 0.718 WFsi10 -0.025 0.042 -0.585 0.558 -0.025 -0.025 WFsi12 ~
WFinat10 0.097 0.047 2.075 0.038 0.085 0.085 WFsi10 0.786 0.044 17.714 0.000 0.743 0.743

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.067 0.031 2.156 0.031 0.207 0.207 .WFinat7 ~~
.WFsi7 0.100 0.016 6.264 0.000 0.359 0.359 .WFinat10 ~~
.WFsi10 0.071 0.015 4.852 0.000 0.258 0.258 .WFinat12 ~~
.WFsi12 0.077 0.012 6.548 0.000 0.393 0.393 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.397 0.028 14.000 0.000 0.940 0.940 RIinat3 0.456 0.027 16.973 0.000 1.013 1.013 RIinat4 0.353 0.025 13.965 0.000 0.787 0.787 RIinat5 0.224 0.024 9.361 0.000 0.491 0.491 RIinat6 0.227 0.026 8.880 0.000 0.554 0.554 RIinat7 0.349 0.025 14.171 0.000 0.793 0.793 RIinat8 0.132 0.025 5.304 0.000 0.301 0.301 RIinat9 0.290 0.026 10.994 0.000 0.624 0.624 RIsi1 0.142 0.033 4.330 0.000 0.418 0.418 RIsi2 0.251 0.033 7.513 0.000 0.650 0.650 RIsi3 0.142 0.031 4.537 0.000 0.391 0.391 RIsi4 0.100 0.026 3.876 0.000 0.213 0.213 RIsi5 0.285 0.035 8.177 0.000 0.648 0.648 RIsi6 0.248 0.033 7.550 0.000 0.593 0.593 RIinat2 ~~
RIinat3 0.366 0.028 12.999 0.000 0.922 0.922 RIinat4 0.326 0.026 12.448 0.000 0.824 0.824 RIinat5 0.180 0.024 7.366 0.000 0.447 0.447 RIinat6 0.219 0.026 8.416 0.000 0.607 0.607 RIinat7 0.319 0.025 12.521 0.000 0.822 0.822 RIinat8 0.144 0.025 5.822 0.000 0.373 0.373 RIinat9 0.253 0.027 9.460 0.000 0.618 0.618 RIsi1 0.110 0.034 3.264 0.001 0.367 0.367 RIsi2 0.210 0.035 6.018 0.000 0.616 0.616 RIsi3 0.121 0.032 3.738 0.000 0.377 0.377 RIsi4 0.081 0.027 3.019 0.003 0.196 0.196 RIsi5 0.230 0.036 6.304 0.000 0.593 0.593 RIsi6 0.208 0.035 5.968 0.000 0.566 0.566 RIinat3 ~~
RIinat4 0.338 0.025 13.426 0.000 0.802 0.802 RIinat5 0.212 0.024 8.968 0.000 0.494 0.494 RIinat6 0.215 0.025 8.526 0.000 0.557 0.557 RIinat7 0.309 0.025 12.499 0.000 0.745 0.745 RIinat8 0.142 0.025 5.804 0.000 0.345 0.345 RIinat9 0.275 0.026 10.614 0.000 0.631 0.631 RIsi1 0.127 0.033 3.898 0.000 0.398 0.398 RIsi2 0.220 0.033 6.651 0.000 0.606 0.606 RIsi3 0.136 0.031 4.357 0.000 0.398 0.398 RIsi4 0.074 0.026 2.897 0.004 0.168 0.168 RIsi5 0.258 0.035 7.436 0.000 0.623 0.623 RIsi6 0.221 0.033 6.776 0.000 0.562 0.562 RIinat4 ~~
RIinat5 0.246 0.022 11.123 0.000 0.574 0.574 RIinat6 0.269 0.024 11.342 0.000 0.700 0.700 RIinat7 0.337 0.023 14.618 0.000 0.816 0.816 RIinat8 0.187 0.023 8.194 0.000 0.455 0.455 RIinat9 0.282 0.024 11.551 0.000 0.648 0.648 RIsi1 0.112 0.029 3.806 0.000 0.352 0.352 RIsi2 0.231 0.030 7.619 0.000 0.639 0.639 RIsi3 0.143 0.028 5.078 0.000 0.421 0.421 RIsi4 0.070 0.024 2.947 0.003 0.160 0.160 RIsi5 0.265 0.032 8.275 0.000 0.643 0.643 RIsi6 0.208 0.030 6.942 0.000 0.531 0.531 RIinat5 ~~
RIinat6 0.238 0.022 10.966 0.000 0.610 0.610 RIinat7 0.214 0.022 9.783 0.000 0.510 0.510 RIinat8 0.172 0.021 7.999 0.000 0.410 0.410 RIinat9 0.300 0.023 12.985 0.000 0.678 0.678 RIsi1 0.174 0.027 6.342 0.000 0.538 0.538 RIsi2 0.256 0.028 9.268 0.000 0.695 0.695 RIsi3 0.129 0.026 5.037 0.000 0.372 0.372 RIsi4 0.196 0.023 8.568 0.000 0.436 0.436 RIsi5 0.296 0.030 9.912 0.000 0.707 0.707 RIsi6 0.272 0.028 9.709 0.000 0.683 0.683 RIinat6 ~~
RIinat7 0.243 0.023 10.429 0.000 0.643 0.643 RIinat8 0.298 0.023 13.101 0.000 0.793 0.793 RIinat9 0.281 0.025 11.384 0.000 0.707 0.707 RIsi1 0.081 0.030 2.667 0.008 0.277 0.277 RIsi2 0.208 0.030 6.979 0.000 0.629 0.629 RIsi3 0.105 0.028 3.746 0.000 0.339 0.339 RIsi4 0.079 0.023 3.374 0.001 0.196 0.196 RIsi5 0.231 0.032 7.255 0.000 0.615 0.615 RIsi6 0.182 0.030 6.071 0.000 0.509 0.509 RIinat7 ~~
RIinat8 0.144 0.022 6.436 0.000 0.357 0.357 RIinat9 0.254 0.024 10.464 0.000 0.595 0.595 RIsi1 0.151 0.029 5.225 0.000 0.482 0.482 RIsi2 0.241 0.030 8.076 0.000 0.676 0.676 RIsi3 0.147 0.027 5.389 0.000 0.442 0.442 RIsi4 0.137 0.023 5.829 0.000 0.315 0.315 RIsi5 0.265 0.032 8.244 0.000 0.655 0.655 RIsi6 0.283 0.029 9.628 0.000 0.735 0.735 RIinat8 ~~
RIinat9 0.290 0.024 12.205 0.000 0.680 0.680 RIsi1 0.117 0.029 4.066 0.000 0.374 0.374 RIsi2 0.196 0.029 6.842 0.000 0.553 0.553 RIsi3 0.111 0.027 4.115 0.000 0.333 0.333 RIsi4 0.064 0.023 2.820 0.005 0.149 0.149 RIsi5 0.210 0.031 6.854 0.000 0.520 0.520 RIsi6 0.110 0.029 3.765 0.000 0.287 0.287 RIinat9 ~~
RIsi1 0.166 0.032 5.193 0.000 0.504 0.504 RIsi2 0.227 0.032 7.144 0.000 0.605 0.605 RIsi3 0.136 0.030 4.601 0.000 0.387 0.387 RIsi4 0.146 0.025 5.810 0.000 0.318 0.318 RIsi5 0.252 0.035 7.298 0.000 0.591 0.591 RIsi6 0.261 0.032 8.231 0.000 0.643 0.643 RIsi1 ~~
RIsi2 0.088 0.063 1.394 0.163 0.322 0.322 RIsi3 0.133 0.059 2.268 0.023 0.515 0.515 RIsi4 0.077 0.046 1.689 0.091 0.230 0.230 RIsi5 0.102 0.067 1.522 0.128 0.326 0.326 RIsi6 0.120 0.060 1.991 0.046 0.405 0.405 RIsi2 ~~
RIsi3 0.047 0.060 0.781 0.435 0.159 0.159 RIsi4 0.172 0.047 3.664 0.000 0.451 0.451 RIsi5 0.351 0.070 5.003 0.000 0.989 0.989 RIsi6 0.213 0.064 3.331 0.001 0.630 0.630 RIsi3 ~~
RIsi4 -0.046 0.042 -1.101 0.271 -0.128 -0.128 RIsi5 0.057 0.063 0.902 0.367 0.171 0.171 RIsi6 0.018 0.056 0.326 0.744 0.058 0.058 RIsi4 ~~
RIsi5 0.143 0.051 2.808 0.005 0.330 0.330 RIsi6 0.333 0.046 7.216 0.000 0.809 0.809 RIsi5 ~~
RIsi6 0.210 0.068 3.109 0.002 0.548 0.548

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.208 0.208 0.208 .pe81m7 0.159 0.159 0.159 .pe81m10 0.169 0.169 0.169 .pe81m12 0.109 0.109 0.109 .pe82m5 0.280 0.280 0.280 .pe82m7 0.226 0.226 0.226 .pe82m10 0.237 0.237 0.237 .pe82m12 0.171 0.171 0.171 .pe83m5 0.256 0.256 0.256 .pe83m7 0.206 0.206 0.206 .pe83m10 0.217 0.217 0.217 .pe83m12 0.155 0.155 0.155 .pe86m5 0.338 0.338 0.338 .pe86m7 0.300 0.300 0.300 .pe86m10 0.308 0.308 0.308 .pe86m12 0.261 0.261 0.261 .pe87m5 0.397 0.397 0.397 .pe87m7 0.370 0.370 0.370 .pe87m10 0.376 0.376 0.376 .pe87m12 0.344 0.344 0.344 .pe88m5 0.404 0.404 0.404 .pe88m7 0.366 0.366 0.366 .pe88m10 0.374 0.374 0.374 .pe88m12 0.327 0.327 0.327 .pe89m5 0.381 0.381 0.381 .pe89m7 0.348 0.348 0.348 .pe89m10 0.355 0.355 0.355 .pe89m12 0.314 0.314 0.314 .pe90m5 0.405 0.405 0.405 .pe90m7 0.375 0.375 0.375 .pe90m10 0.382 0.382 0.382 .pe90m12 0.345 0.345 0.345 .pe91m5 0.319 0.319 0.319 .pe91m7 0.283 0.283 0.283 .pe91m10 0.291 0.291 0.291 .pe91m12 0.247 0.247 0.247 .pe2m5 0.424 0.424 0.424 .pe2m7 0.339 0.339 0.339 .pe2m10 0.349 0.349 0.349 .pe2m12 0.300 0.300 0.300 .pe4m5 0.319 0.319 0.319 .pe4m7 0.225 0.225 0.225 .pe4m10 0.237 0.237 0.237 .pe4m12 0.182 0.182 0.182 .pe7m5 0.418 0.418 0.418 .pe7m7 0.340 0.340 0.340 .pe7m10 0.350 0.350 0.350 .pe7m12 0.305 0.305 0.305 .pe11m5 0.372 0.372 0.372 .pe11m7 0.330 0.330 0.330 .pe11m10 0.335 0.335 0.335 .pe11m12 0.311 0.311 0.311 .pe13m5 0.236 0.236 0.236 .pe13m7 0.145 0.145 0.145 .pe13m10 0.156 0.156 0.156 .pe13m12 0.103 0.103 0.103 .pe25m5 0.311 0.311 0.311 .pe25m7 0.228 0.228 0.228 .pe25m10 0.239 0.239 0.239 .pe25m12 0.191 0.191 0.191 RIinat1 0.479 0.028 17.103 0.000 1.000 1.000 RIinat2 0.372 0.031 12.150 0.000 1.000 1.000 RIinat3 0.423 0.028 15.016 0.000 1.000 1.000 RIinat4 0.420 0.025 17.128 0.000 1.000 1.000 RIinat5 0.435 0.021 20.304 0.000 1.000 1.000 RIinat6 0.351 0.026 13.658 0.000 1.000 1.000 RIinat7 0.405 0.025 16.321 0.000 1.000 1.000 RIinat8 0.403 0.023 17.877 0.000 1.000 1.000 RIinat9 0.451 0.027 16.882 0.000 1.000 1.000 RIsi1 0.242 0.066 3.635 0.000 1.000 1.000 RIsi2 0.312 0.069 4.498 0.000 1.000 1.000 RIsi3 0.275 0.058 4.754 0.000 1.000 1.000 RIsi4 0.464 0.037 12.593 0.000 1.000 1.000 RIsi5 0.404 0.074 5.444 0.000 1.000 1.000 RIsi6 0.365 0.065 5.611 0.000 1.000 1.000 WFinat5 0.313 0.028 11.173 0.000 1.000 1.000 .WFinat7 0.286 0.018 16.298 0.000 0.790 0.790 .WFinat10 0.284 0.017 16.775 0.000 0.806 0.806 .WFinat12 0.203 0.014 14.539 0.000 0.492 0.492 WFsi5 0.335 0.069 4.842 0.000 1.000 1.000 .WFsi7 0.273 0.036 7.659 0.000 0.651 0.651 .WFsi10 0.264 0.034 7.734 0.000 0.644 0.644 .WFsi12 0.188 0.028 6.775 0.000 0.410 0.410

S2 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.991 (>0.95) Change in CFI: 0.001 (increase) - better fit Tucker-Lewis Index (TLI) 0.990 (>0.95) Change in TLI: 0.001 (increase) - better fit RMSEA 0.015 (≤ 0.06) Change in RMSEA: 0.001 (decrease) - better fit 90 Percent confidence interval - lower 0.014 90 Percent confidence interval - upper 0.016
SRMR 0.036 (≤ 0.08) Change in SRMR: 0.003 (increase) - worse fit (to be expected)

We can look at the best fitting model and the difference in fit indices by using the compareFit function from semTools.

summary(semTools::compareFit(RICLPM_multi_inat_S1.fit, RICLPM_multi_inat_S2.fit, nested = TRUE)) #† indicates the best fitting model - using the robust statistics

Nested Model Comparison

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

lavaan NOTE: The “Chisq” column contains standard test statistics, not the robust test that should be reported per model. A robust difference test is a function of two standard (not robust) statistics.

                       Df AIC BIC  Chisq Chisq diff Df diff Pr(>Chisq)

RICLPM_multi_inat_S1.fit 1574 1977.3
RICLPM_multi_inat_S2.fit 1613 2267.9 72.759 39 0.0008346 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Model Fit Indices

                     chisq.scaled df.scaled pvalue.scaled rmsea.scaled

RICLPM_multi_inat_S1.fit 2426.651 1574 .000 .016 RICLPM_multi_inat_S2.fit 2385.499† 1613 .000 .015† cfi.scaled tli.scaled srmr RICLPM_multi_inat_S1.fit .990 .989 .032† RICLPM_multi_inat_S2.fit .991† .990† .035

Differences in Fit Indices

                                                df.scaled rmsea.scaled

RICLPM_multi_inat_S2.fit - RICLPM_multi_inat_S1.fit 39 -0.001 cfi.scaled tli.scaled srmr RICLPM_multi_inat_S2.fit - RICLPM_multi_inat_S1.fit 0.001 0.001 0.002

Now we need to conduct a Likelihood ratio test to see if the constrained model is a significantly worse fit than the free loading model. By constraining the factor loadings over time we can assume that the items load onto the same construct in the same way at each time point.

Really important to note that the Chi Square p value is very likely to be significant when using large samples. We should consider the change in model fit as well as the Chi square.

lavTestLRT(RICLPM_multi_inat_S1.fit, RICLPM_multi_inat_S2.fit)

Significantly worse fit to include the restrictions, p=0.001017.

However this is almost never going to be nonsignificant, we can assume here that because the S2 model did not show substantial decrease in model fit (actually showed better fit than the S1 model), that weak invariance holds here.

Now we will move onto strong invariance tests.

RICLPM_multi_inat_S3: Inattention step 3

Multiple response items RICLPM mother report inattention ADHD symptoms and social isolation: Step 3

As full or partial weak invariance is supported, the next step is to test for strong (scalar) invariance, or equivalence of item intercepts, for weak invariant items. Strong invariance means that mean differences in the latent construct capture all mean differences in the shared variance of the items. Strong invariance is tested by constraining the item intercepts to be equivalent in the two groups - with the restraint applied in the weak invariance model retained. Any items that had unequal loadings in the weak invariance model (and were allowed to vary) should be allowed to vary in the strong invariance model because it is meaningless to test for equal item intercepts if the factor loadings of the items differs across groups (Putnick and Bornstein, 2016).

Fitting a model with constraints to ensure strong factorial invariance, with a random intercept for each indicator separately.

RICLPM_multi_inat_S3 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (mother report)
  RIinat1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIinat2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIinat3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIinat4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIinat5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIinat6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIinat7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIinat8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIinat9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  WFinat5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5
  WFinat7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7
  WFinat10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10
  WFinat12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  WFsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  WFsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  WFsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  pe81m5 + pe81m7 + pe81m10 + pe81m12 ~ p*1
  pe82m5 + pe82m7 + pe82m10 + pe82m12 ~ q*1
  pe83m5 + pe83m7 + pe83m10 + pe83m12 ~ r*1
  pe86m5 + pe86m7 + pe86m10 + pe86m12 ~ s*1
  pe87m5 + pe87m7 + pe87m10 + pe87m12 ~ t*1
  pe88m5 + pe88m7 + pe88m10 + pe88m12 ~ u*1
  pe89m5 + pe89m7 + pe89m10 + pe89m12 ~ v*1
  pe90m5 + pe90m7 + pe90m10 + pe90m12 ~ w*1
  pe91m5 + pe91m7 + pe91m10 + pe91m12 ~ x*1
  
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ y*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ z*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ aa*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ ab*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ ac*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ ad*1
  
  # Free latent means from timepoint = 2 (age 7) onward. 
  # Only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  WFinat7 + WFinat10 + WFinat12 + WFsi7 + WFsi10 + WFsi12 ~ 1
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPM_multi_inat_S3.fit <- cfa(RICLPM_multi_inat_S3, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise')

RICLPM_multi_inat_S3.fit.summary <- summary(RICLPM_multi_inat_S3.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 156 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 382 Number of equality constraints 84

Number of observations 2232 Number of missing patterns 48

Model Test User Model: Standard Robust Test Statistic 2267.886 2359.454 Degrees of freedom 1592 1592 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.531 Shift parameter 878.530 simple second-order correction

Model Test Baseline Model:

Test statistic 358266.126 88092.703 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.130

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.998 0.991 Tucker-Lewis Index (TLI) 0.998 0.990

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.014 0.015 90 Percent confidence interval - lower 0.012 0.013 90 Percent confidence interval - upper 0.015 0.016 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.035 0.035

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
pe81m5 1.000 0.692 0.692 pe81m7 1.000 0.692 0.692 pe81m10 1.000 0.692 0.692 pe81m12 1.000 0.692 0.692 RIinat2 =~
pe82m5 1.000 0.610 0.610 pe82m7 1.000 0.610 0.610 pe82m10 1.000 0.610 0.610 pe82m12 1.000 0.610 0.610 RIinat3 =~
pe83m5 1.000 0.651 0.651 pe83m7 1.000 0.651 0.651 pe83m10 1.000 0.651 0.651 pe83m12 1.000 0.651 0.651 RIinat4 =~
pe86m5 1.000 0.648 0.648 pe86m7 1.000 0.648 0.648 pe86m10 1.000 0.648 0.648 pe86m12 1.000 0.648 0.648 RIinat5 =~
pe87m5 1.000 0.660 0.660 pe87m7 1.000 0.660 0.660 pe87m10 1.000 0.660 0.660 pe87m12 1.000 0.660 0.660 RIinat6 =~
pe88m5 1.000 0.592 0.592 pe88m7 1.000 0.592 0.592 pe88m10 1.000 0.592 0.592 pe88m12 1.000 0.592 0.592 RIinat7 =~
pe89m5 1.000 0.637 0.637 pe89m7 1.000 0.637 0.637 pe89m10 1.000 0.637 0.637 pe89m12 1.000 0.637 0.637 RIinat8 =~
pe90m5 1.000 0.635 0.635 pe90m7 1.000 0.635 0.635 pe90m10 1.000 0.635 0.635 pe90m12 1.000 0.635 0.635 RIinat9 =~
pe91m5 1.000 0.671 0.671 pe91m7 1.000 0.671 0.671 pe91m10 1.000 0.671 0.671 pe91m12 1.000 0.671 0.671 RIsi1 =~
pe2m5 1.000 0.491 0.491 pe2m7 1.000 0.491 0.491 pe2m10 1.000 0.491 0.491 pe2m12 1.000 0.491 0.491 RIsi2 =~
pe4m5 1.000 0.559 0.559 pe4m7 1.000 0.559 0.559 pe4m10 1.000 0.559 0.559 pe4m12 1.000 0.559 0.559 RIsi3 =~
pe7m5 1.000 0.524 0.524 pe7m7 1.000 0.524 0.524 pe7m10 1.000 0.524 0.524 pe7m12 1.000 0.524 0.524 RIsi4 =~
pe11m5 1.000 0.681 0.681 pe11m7 1.000 0.681 0.681 pe11m10 1.000 0.681 0.681 pe11m12 1.000 0.681 0.681 RIsi5 =~
pe13m5 1.000 0.636 0.636 pe13m7 1.000 0.636 0.636 pe13m10 1.000 0.636 0.636 pe13m12 1.000 0.636 0.636 RIsi6 =~
pe25m5 1.000 0.604 0.604 pe25m7 1.000 0.604 0.604 pe25m10 1.000 0.604 0.604 pe25m12 1.000 0.604 0.604 WFinat5 =~
pe81m5 (a) 1.000 0.560 0.560 pe82m5 (b) 1.054 0.029 35.865 0.000 0.590 0.590 pe83m5 (c) 1.012 0.027 37.369 0.000 0.566 0.566 pe86m5 (d) 0.880 0.032 27.904 0.000 0.492 0.492 pe87m5 (e) 0.733 0.036 20.340 0.000 0.410 0.410 pe88m5 (f) 0.885 0.036 24.310 0.000 0.495 0.495 pe89m5 (g) 0.826 0.035 23.652 0.000 0.462 0.462 pe90m5 (h) 0.783 0.037 20.944 0.000 0.438 0.438 pe91m5 (i) 0.857 0.037 22.931 0.000 0.480 0.480 WFinat7 =~
pe81m7 (a) 1.000 0.602 0.602 pe82m7 (b) 1.054 0.029 35.865 0.000 0.634 0.634 pe83m7 (c) 1.012 0.027 37.369 0.000 0.609 0.609 pe86m7 (d) 0.880 0.032 27.904 0.000 0.529 0.529 pe87m7 (e) 0.733 0.036 20.340 0.000 0.441 0.441 pe88m7 (f) 0.885 0.036 24.310 0.000 0.532 0.532 pe89m7 (g) 0.826 0.035 23.652 0.000 0.497 0.497 pe90m7 (h) 0.783 0.037 20.944 0.000 0.471 0.471 pe91m7 (i) 0.857 0.037 22.931 0.000 0.516 0.516 WFinat10 =~
pe81m10 (a) 1.000 0.593 0.593 pe82m10 (b) 1.054 0.029 35.865 0.000 0.625 0.625 pe83m10 (c) 1.012 0.027 37.369 0.000 0.600 0.600 pe86m10 (d) 0.880 0.032 27.904 0.000 0.522 0.522 pe87m10 (e) 0.733 0.036 20.340 0.000 0.435 0.435 pe88m10 (f) 0.885 0.036 24.310 0.000 0.525 0.525 pe89m10 (g) 0.826 0.035 23.652 0.000 0.490 0.490 pe90m10 (h) 0.783 0.037 20.944 0.000 0.464 0.464 pe91m10 (i) 0.857 0.037 22.931 0.000 0.508 0.508 WFinat12 =~
pe81m12 (a) 1.000 0.642 0.642 pe82m12 (b) 1.054 0.029 35.865 0.000 0.676 0.676 pe83m12 (c) 1.012 0.027 37.369 0.000 0.649 0.649 pe86m12 (d) 0.880 0.032 27.904 0.000 0.564 0.564 pe87m12 (e) 0.733 0.036 20.340 0.000 0.470 0.470 pe88m12 (f) 0.885 0.036 24.310 0.000 0.568 0.568 pe89m12 (g) 0.826 0.035 23.652 0.000 0.530 0.530 pe90m12 (h) 0.783 0.037 20.944 0.000 0.502 0.502 pe91m12 (i) 0.857 0.037 22.931 0.000 0.550 0.550 WFsi5 =~
pe2m5 (j) 1.000 0.579 0.579 pe4m5 (k) 1.050 0.081 12.891 0.000 0.607 0.607 pe7m5 (l) 0.958 0.079 12.078 0.000 0.554 0.554 pe11m5 (m) 0.701 0.069 10.099 0.000 0.406 0.406 pe13m5 (n) 1.037 0.092 11.270 0.000 0.600 0.600 pe25m5 (o) 0.985 0.086 11.441 0.000 0.570 0.570 WFsi7 =~
pe2m7 (j) 1.000 0.648 0.648 pe4m7 (k) 1.050 0.081 12.891 0.000 0.680 0.680 pe7m7 (l) 0.958 0.079 12.078 0.000 0.621 0.621 pe11m7 (m) 0.701 0.069 10.099 0.000 0.454 0.454 pe13m7 (n) 1.037 0.092 11.270 0.000 0.672 0.672 pe25m7 (o) 0.985 0.086 11.441 0.000 0.638 0.638 WFsi10 =~
pe2m10 (j) 1.000 0.640 0.640 pe4m10 (k) 1.050 0.081 12.891 0.000 0.672 0.672 pe7m10 (l) 0.958 0.079 12.078 0.000 0.613 0.613 pe11m10 (m) 0.701 0.069 10.099 0.000 0.449 0.449 pe13m10 (n) 1.037 0.092 11.270 0.000 0.663 0.663 pe25m10 (o) 0.985 0.086 11.441 0.000 0.630 0.630 WFsi12 =~
pe2m12 (j) 1.000 0.677 0.677 pe4m12 (k) 1.050 0.081 12.891 0.000 0.711 0.711 pe7m12 (l) 0.958 0.079 12.078 0.000 0.648 0.648 pe11m12 (m) 0.701 0.069 10.099 0.000 0.475 0.475 pe13m12 (n) 1.037 0.092 11.270 0.000 0.702 0.702 pe25m12 (o) 0.985 0.086 11.441 0.000 0.667 0.667

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.067 0.031 2.156 0.031 0.207 0.207 .WFinat7 ~~
.WFsi7 0.100 0.016 6.264 0.000 0.359 0.359 .WFinat10 ~~
.WFsi10 0.071 0.015 4.852 0.000 0.258 0.258 .WFinat12 ~~
.WFsi12 0.077 0.012 6.548 0.000 0.393 0.393 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.397 0.028 14.000 0.000 0.940 0.940 RIinat3 0.456 0.027 16.973 0.000 1.013 1.013 RIinat4 0.353 0.025 13.965 0.000 0.787 0.787 RIinat5 0.224 0.024 9.361 0.000 0.491 0.491 RIinat6 0.227 0.026 8.880 0.000 0.554 0.554 RIinat7 0.349 0.025 14.171 0.000 0.793 0.793 RIinat8 0.132 0.025 5.304 0.000 0.301 0.301 RIinat9 0.290 0.026 10.994 0.000 0.624 0.624 RIsi1 0.142 0.033 4.330 0.000 0.418 0.418 RIsi2 0.251 0.033 7.513 0.000 0.650 0.650 RIsi3 0.142 0.031 4.537 0.000 0.391 0.391 RIsi4 0.100 0.026 3.876 0.000 0.213 0.213 RIsi5 0.285 0.035 8.177 0.000 0.648 0.648 RIsi6 0.248 0.033 7.550 0.000 0.593 0.593 RIinat2 ~~
RIinat3 0.366 0.028 12.999 0.000 0.922 0.922 RIinat4 0.326 0.026 12.448 0.000 0.824 0.824 RIinat5 0.180 0.024 7.366 0.000 0.447 0.447 RIinat6 0.219 0.026 8.416 0.000 0.607 0.607 RIinat7 0.319 0.025 12.521 0.000 0.822 0.822 RIinat8 0.144 0.025 5.822 0.000 0.373 0.373 RIinat9 0.253 0.027 9.460 0.000 0.618 0.618 RIsi1 0.110 0.034 3.264 0.001 0.367 0.367 RIsi2 0.210 0.035 6.018 0.000 0.616 0.616 RIsi3 0.121 0.032 3.738 0.000 0.377 0.377 RIsi4 0.081 0.027 3.019 0.003 0.196 0.196 RIsi5 0.230 0.036 6.304 0.000 0.593 0.593 RIsi6 0.208 0.035 5.968 0.000 0.566 0.566 RIinat3 ~~
RIinat4 0.338 0.025 13.426 0.000 0.802 0.802 RIinat5 0.212 0.024 8.968 0.000 0.494 0.494 RIinat6 0.215 0.025 8.526 0.000 0.557 0.557 RIinat7 0.309 0.025 12.499 0.000 0.745 0.745 RIinat8 0.142 0.025 5.804 0.000 0.345 0.345 RIinat9 0.275 0.026 10.614 0.000 0.631 0.631 RIsi1 0.127 0.033 3.898 0.000 0.398 0.398 RIsi2 0.220 0.033 6.651 0.000 0.606 0.606 RIsi3 0.136 0.031 4.357 0.000 0.398 0.398 RIsi4 0.074 0.026 2.897 0.004 0.168 0.168 RIsi5 0.258 0.035 7.436 0.000 0.623 0.623 RIsi6 0.221 0.033 6.776 0.000 0.562 0.562 RIinat4 ~~
RIinat5 0.246 0.022 11.123 0.000 0.574 0.574 RIinat6 0.269 0.024 11.342 0.000 0.700 0.700 RIinat7 0.337 0.023 14.618 0.000 0.816 0.816 RIinat8 0.187 0.023 8.194 0.000 0.455 0.455 RIinat9 0.282 0.024 11.551 0.000 0.648 0.648 RIsi1 0.112 0.029 3.806 0.000 0.352 0.352 RIsi2 0.231 0.030 7.619 0.000 0.639 0.639 RIsi3 0.143 0.028 5.078 0.000 0.421 0.421 RIsi4 0.070 0.024 2.947 0.003 0.160 0.160 RIsi5 0.265 0.032 8.275 0.000 0.643 0.643 RIsi6 0.208 0.030 6.942 0.000 0.531 0.531 RIinat5 ~~
RIinat6 0.238 0.022 10.966 0.000 0.610 0.610 RIinat7 0.214 0.022 9.783 0.000 0.510 0.510 RIinat8 0.172 0.021 7.999 0.000 0.410 0.410 RIinat9 0.300 0.023 12.985 0.000 0.678 0.678 RIsi1 0.174 0.027 6.342 0.000 0.538 0.538 RIsi2 0.256 0.028 9.268 0.000 0.695 0.695 RIsi3 0.129 0.026 5.037 0.000 0.372 0.372 RIsi4 0.196 0.023 8.568 0.000 0.436 0.436 RIsi5 0.296 0.030 9.912 0.000 0.707 0.707 RIsi6 0.272 0.028 9.709 0.000 0.683 0.683 RIinat6 ~~
RIinat7 0.243 0.023 10.429 0.000 0.643 0.643 RIinat8 0.298 0.023 13.101 0.000 0.793 0.793 RIinat9 0.281 0.025 11.384 0.000 0.707 0.707 RIsi1 0.081 0.030 2.667 0.008 0.277 0.277 RIsi2 0.208 0.030 6.978 0.000 0.629 0.629 RIsi3 0.105 0.028 3.745 0.000 0.339 0.339 RIsi4 0.079 0.023 3.374 0.001 0.196 0.196 RIsi5 0.231 0.032 7.255 0.000 0.615 0.615 RIsi6 0.182 0.030 6.071 0.000 0.509 0.509 RIinat7 ~~
RIinat8 0.144 0.022 6.436 0.000 0.357 0.357 RIinat9 0.254 0.024 10.464 0.000 0.595 0.595 RIsi1 0.151 0.029 5.225 0.000 0.482 0.482 RIsi2 0.241 0.030 8.076 0.000 0.676 0.676 RIsi3 0.147 0.027 5.389 0.000 0.442 0.442 RIsi4 0.137 0.023 5.829 0.000 0.315 0.315 RIsi5 0.265 0.032 8.244 0.000 0.655 0.655 RIsi6 0.283 0.029 9.628 0.000 0.735 0.735 RIinat8 ~~
RIinat9 0.290 0.024 12.205 0.000 0.680 0.680 RIsi1 0.117 0.029 4.066 0.000 0.374 0.374 RIsi2 0.196 0.029 6.842 0.000 0.553 0.553 RIsi3 0.111 0.027 4.115 0.000 0.333 0.333 RIsi4 0.064 0.023 2.820 0.005 0.149 0.149 RIsi5 0.210 0.031 6.854 0.000 0.520 0.520 RIsi6 0.110 0.029 3.765 0.000 0.287 0.287 RIinat9 ~~
RIsi1 0.166 0.032 5.193 0.000 0.504 0.504 RIsi2 0.227 0.032 7.144 0.000 0.605 0.605 RIsi3 0.136 0.030 4.600 0.000 0.387 0.387 RIsi4 0.146 0.025 5.810 0.000 0.318 0.318 RIsi5 0.252 0.035 7.298 0.000 0.591 0.591 RIsi6 0.261 0.032 8.231 0.000 0.643 0.643 RIsi1 ~~
RIsi2 0.088 0.063 1.394 0.163 0.322 0.322 RIsi3 0.133 0.059 2.267 0.023 0.515 0.515 RIsi4 0.077 0.046 1.689 0.091 0.230 0.230 RIsi5 0.102 0.067 1.522 0.128 0.326 0.326 RIsi6 0.120 0.060 1.991 0.046 0.405 0.405 RIsi2 ~~
RIsi3 0.046 0.060 0.781 0.435 0.159 0.159 RIsi4 0.172 0.047 3.664 0.000 0.451 0.451 RIsi5 0.351 0.070 5.003 0.000 0.989 0.989 RIsi6 0.213 0.064 3.331 0.001 0.630 0.630 RIsi3 ~~
RIsi4 -0.046 0.042 -1.101 0.271 -0.128 -0.128 RIsi5 0.057 0.063 0.902 0.367 0.171 0.171 RIsi6 0.018 0.056 0.326 0.744 0.058 0.058 RIsi4 ~~
RIsi5 0.143 0.051 2.808 0.005 0.330 0.330 RIsi6 0.333 0.046 7.217 0.000 0.809 0.809 RIsi5 ~~
RIsi6 0.210 0.068 3.109 0.002 0.548 0.548

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 (p) -0.003 0.010 -0.330 0.741 -0.003 -0.003 .pe81m7 (p) -0.003 0.010 -0.330 0.741 -0.003 -0.003 .pe81m10 (p) -0.003 0.010 -0.330 0.741 -0.003 -0.003 .pe81m12 (p) -0.003 0.010 -0.330 0.741 -0.003 -0.003 .pe82m5 (q) -0.010 0.009 -1.147 0.251 -0.010 -0.010 .pe82m7 (q) -0.010 0.009 -1.147 0.251 -0.010 -0.010 .pe82m10 (q) -0.010 0.009 -1.147 0.251 -0.010 -0.010 .pe82m12 (q) -0.010 0.009 -1.147 0.251 -0.010 -0.010 .pe83m5 (r) -0.002 0.009 -0.230 0.818 -0.002 -0.002 .pe83m7 (r) -0.002 0.009 -0.230 0.818 -0.002 -0.002 .pe83m10 (r) -0.002 0.009 -0.230 0.818 -0.002 -0.002 .pe83m12 (r) -0.002 0.009 -0.230 0.818 -0.002 -0.002 .pe86m5 (s) 0.002 0.009 0.213 0.831 0.002 0.002 .pe86m7 (s) 0.002 0.009 0.213 0.831 0.002 0.002 .pe86m10 (s) 0.002 0.009 0.213 0.831 0.002 0.002 .pe86m12 (s) 0.002 0.009 0.213 0.831 0.002 0.002 .pe87m5 (t) -0.005 0.012 -0.384 0.701 -0.005 -0.005 .pe87m7 (t) -0.005 0.012 -0.384 0.701 -0.005 -0.005 .pe87m10 (t) -0.005 0.012 -0.384 0.701 -0.005 -0.005 .pe87m12 (t) -0.005 0.012 -0.384 0.701 -0.005 -0.005 .pe88m5 (u) -0.015 0.009 -1.611 0.107 -0.015 -0.015 .pe88m7 (u) -0.015 0.009 -1.611 0.107 -0.015 -0.015 .pe88m10 (u) -0.015 0.009 -1.611 0.107 -0.015 -0.015 .pe88m12 (u) -0.015 0.009 -1.611 0.107 -0.015 -0.015 .pe89m5 (v) -0.028 0.011 -2.705 0.007 -0.028 -0.028 .pe89m7 (v) -0.028 0.011 -2.705 0.007 -0.028 -0.028 .pe89m10 (v) -0.028 0.011 -2.705 0.007 -0.028 -0.028 .pe89m12 (v) -0.028 0.011 -2.705 0.007 -0.028 -0.028 .pe90m5 (w) -0.004 0.010 -0.426 0.670 -0.004 -0.004 .pe90m7 (w) -0.004 0.010 -0.426 0.670 -0.004 -0.004 .pe90m10 (w) -0.004 0.010 -0.426 0.670 -0.004 -0.004 .pe90m12 (w) -0.004 0.010 -0.426 0.670 -0.004 -0.004 .pe91m5 (x) -0.001 0.011 -0.126 0.899 -0.001 -0.001 .pe91m7 (x) -0.001 0.011 -0.126 0.899 -0.001 -0.001 .pe91m10 (x) -0.001 0.011 -0.126 0.899 -0.001 -0.001 .pe91m12 (x) -0.001 0.011 -0.126 0.899 -0.001 -0.001 .pe2m5 (y) 0.019 0.015 1.237 0.216 0.019 0.019 .pe2m7 (y) 0.019 0.015 1.237 0.216 0.019 0.019 .pe2m10 (y) 0.019 0.015 1.237 0.216 0.019 0.019 .pe2m12 (y) 0.019 0.015 1.237 0.216 0.019 0.019 .pe4m5 (z) 0.038 0.014 2.654 0.008 0.038 0.038 .pe4m7 (z) 0.038 0.014 2.654 0.008 0.038 0.038 .pe4m10 (z) 0.038 0.014 2.654 0.008 0.038 0.038 .pe4m12 (z) 0.038 0.014 2.654 0.008 0.038 0.038 .pe7m5 (aa) 0.035 0.014 2.475 0.013 0.035 0.035 .pe7m7 (aa) 0.035 0.014 2.475 0.013 0.035 0.035 .pe7m10 (aa) 0.035 0.014 2.475 0.013 0.035 0.035 .pe7m12 (aa) 0.035 0.014 2.475 0.013 0.035 0.035 .pe11m5 (ab) 0.056 0.015 3.766 0.000 0.056 0.056 .pe11m7 (ab) 0.056 0.015 3.766 0.000 0.056 0.056 .pe11m10 (ab) 0.056 0.015 3.766 0.000 0.056 0.056 .pe11m12 (ab) 0.056 0.015 3.766 0.000 0.056 0.056 .pe13m5 (ac) 0.018 0.021 0.879 0.379 0.018 0.018 .pe13m7 (ac) 0.018 0.021 0.879 0.379 0.018 0.018 .pe13m10 (ac) 0.018 0.021 0.879 0.379 0.018 0.018 .pe13m12 (ac) 0.018 0.021 0.879 0.379 0.018 0.018 .pe25m5 (ad) 0.009 0.018 0.503 0.615 0.009 0.009 .pe25m7 (ad) 0.009 0.018 0.503 0.615 0.009 0.009 .pe25m10 (ad) 0.009 0.018 0.503 0.615 0.009 0.009 .pe25m12 (ad) 0.009 0.018 0.503 0.615 0.009 0.009 .WFinat7 0.007 0.016 0.446 0.655 0.012 0.012 .WFinat10 0.004 0.016 0.240 0.810 0.006 0.006 .WFinat12 0.001 0.014 0.107 0.915 0.002 0.002 .WFsi7 -0.029 0.025 -1.141 0.254 -0.044 -0.044 .WFsi10 -0.013 0.022 -0.604 0.546 -0.021 -0.021 .WFsi12 -0.007 0.023 -0.326 0.744 -0.011 -0.011 RIinat1 0.000 0.000 0.000 RIinat2 0.000 0.000 0.000 RIinat3 0.000 0.000 0.000 RIinat4 0.000 0.000 0.000 RIinat5 0.000 0.000 0.000 RIinat6 0.000 0.000 0.000 RIinat7 0.000 0.000 0.000 RIinat8 0.000 0.000 0.000 RIinat9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5|t1 0.255 0.021 11.910 0.000 0.255 0.255 pe81m5|t2 1.093 0.027 40.333 0.000 1.093 1.093 pe81m7|t1 0.310 0.019 16.087 0.000 0.310 0.310 pe81m7|t2 1.187 0.023 51.547 0.000 1.187 1.187 pe81m10|t1 0.396 0.019 21.241 0.000 0.396 0.396 pe81m10|t2 1.219 0.023 52.422 0.000 1.219 1.219 pe81m12|t1 0.432 0.019 22.887 0.000 0.432 0.432 pe81m12|t2 1.256 0.023 53.805 0.000 1.256 1.256 pe82m5|t1 0.087 0.022 4.004 0.000 0.087 0.087 pe82m5|t2 1.060 0.027 39.041 0.000 1.060 1.060 pe82m7|t1 0.191 0.020 9.389 0.000 0.191 0.191 pe82m7|t2 1.255 0.024 51.258 0.000 1.255 1.255 pe82m10|t1 0.224 0.021 10.848 0.000 0.224 0.224 pe82m10|t2 1.326 0.026 51.842 0.000 1.326 1.326 pe82m12|t1 0.306 0.020 15.344 0.000 0.306 0.306 pe82m12|t2 1.368 0.026 53.216 0.000 1.368 1.368 pe83m5|t1 -0.112 0.021 -5.234 0.000 -0.112 -0.112 pe83m5|t2 0.760 0.024 31.692 0.000 0.760 0.760 pe83m7|t1 -0.019 0.020 -0.986 0.324 -0.019 -0.019 pe83m7|t2 0.968 0.021 46.372 0.000 0.968 0.968 pe83m10|t1 -0.019 0.020 -0.974 0.330 -0.019 -0.019 pe83m10|t2 1.018 0.022 47.224 0.000 1.018 1.018 pe83m12|t1 0.057 0.020 2.878 0.004 0.057 0.057 pe83m12|t2 1.103 0.022 49.710 0.000 1.103 1.103 pe86m5|t1 0.064 0.022 2.902 0.004 0.064 0.064 pe86m5|t2 1.142 0.028 40.225 0.000 1.142 1.142 pe86m7|t1 0.155 0.021 7.546 0.000 0.155 0.155 pe86m7|t2 1.316 0.027 49.379 0.000 1.316 1.316 pe86m10|t1 0.163 0.021 7.967 0.000 0.163 0.163 pe86m10|t2 1.295 0.027 48.239 0.000 1.295 1.295 pe86m12|t1 0.088 0.021 4.272 0.000 0.088 0.088 pe86m12|t2 1.349 0.027 49.453 0.000 1.349 1.349 pe87m5|t1 0.311 0.023 13.508 0.000 0.311 0.311 pe87m5|t2 1.491 0.035 42.438 0.000 1.491 1.491 pe87m7|t1 0.267 0.022 11.947 0.000 0.267 0.267 pe87m7|t2 1.640 0.037 44.902 0.000 1.640 1.640 pe87m10|t1 0.406 0.023 18.058 0.000 0.406 0.406 pe87m10|t2 1.765 0.040 44.255 0.000 1.765 1.765 pe87m12|t1 0.367 0.022 16.854 0.000 0.367 0.367 pe87m12|t2 1.699 0.038 44.874 0.000 1.699 1.699 pe88m5|t1 0.388 0.023 17.084 0.000 0.388 0.388 pe88m5|t2 1.123 0.029 38.896 0.000 1.123 1.123 pe88m7|t1 0.319 0.021 15.228 0.000 0.319 0.319 pe88m7|t2 1.274 0.027 46.924 0.000 1.274 1.274 pe88m10|t1 0.048 0.021 2.301 0.021 0.048 0.048 pe88m10|t2 1.006 0.023 43.563 0.000 1.006 1.006 pe88m12|t1 0.002 0.021 0.103 0.918 0.002 0.002 pe88m12|t2 0.935 0.022 41.987 0.000 0.935 0.935 pe89m5|t1 0.450 0.023 19.649 0.000 0.450 0.450 pe89m5|t2 1.262 0.031 41.195 0.000 1.262 1.262 pe89m7|t1 0.476 0.021 23.095 0.000 0.476 0.476 pe89m7|t2 1.386 0.030 46.684 0.000 1.386 1.386 pe89m10|t1 0.442 0.021 20.963 0.000 0.442 0.442 pe89m10|t2 1.377 0.030 46.385 0.000 1.377 1.377 pe89m12|t1 0.439 0.021 20.798 0.000 0.439 0.439 pe89m12|t2 1.362 0.029 47.705 0.000 1.362 1.362 pe90m5|t1 0.050 0.022 2.313 0.021 0.050 0.050 pe90m5|t2 0.802 0.025 32.503 0.000 0.802 0.802 pe90m7|t1 0.046 0.021 2.178 0.029 0.046 0.046 pe90m7|t2 0.989 0.024 41.973 0.000 0.989 0.989 pe90m10|t1 0.043 0.021 2.079 0.038 0.043 0.043 pe90m10|t2 0.940 0.023 40.304 0.000 0.940 0.940 pe90m12|t1 0.080 0.021 3.879 0.000 0.080 0.080 pe90m12|t2 0.971 0.023 41.682 0.000 0.971 0.971 pe91m5|t1 0.578 0.023 24.973 0.000 0.578 0.578 pe91m5|t2 1.442 0.033 43.605 0.000 1.442 1.442 pe91m7|t1 0.575 0.021 27.076 0.000 0.575 0.575 pe91m7|t2 1.539 0.031 50.396 0.000 1.539 1.539 pe91m10|t1 0.653 0.022 30.079 0.000 0.653 0.653 pe91m10|t2 1.610 0.034 47.811 0.000 1.610 1.610 pe91m12|t1 0.578 0.021 27.554 0.000 0.578 0.578 pe91m12|t2 1.628 0.034 47.632 0.000 1.628 1.628 pe2m5|t1 1.384 0.033 41.992 0.000 1.384 1.384 pe2m5|t2 2.386 0.074 32.359 0.000 2.386 2.386 pe2m7|t1 1.166 0.032 35.942 0.000 1.166 1.166 pe2m7|t2 2.249 0.058 38.958 0.000 2.249 2.249 pe2m10|t1 0.996 0.031 32.536 0.000 0.996 0.996 pe2m10|t2 2.125 0.052 40.553 0.000 2.125 2.125 pe2m12|t1 1.118 0.031 35.971 0.000 1.118 1.118 pe2m12|t2 2.227 0.057 39.298 0.000 2.227 2.227 pe4m5|t1 1.045 0.027 38.664 0.000 1.045 1.045 pe4m5|t2 2.168 0.057 38.183 0.000 2.168 2.168 pe4m7|t1 1.020 0.031 32.734 0.000 1.020 1.020 pe4m7|t2 2.267 0.055 41.477 0.000 2.267 2.267 pe4m10|t1 0.924 0.030 30.617 0.000 0.924 0.924 pe4m10|t2 2.154 0.047 45.804 0.000 2.154 2.154 pe4m12|t1 0.909 0.032 28.185 0.000 0.909 0.909 pe4m12|t2 2.231 0.052 43.176 0.000 2.231 2.231 pe7m5|t1 0.869 0.026 33.171 0.000 0.869 0.869 pe7m5|t2 1.993 0.050 40.193 0.000 1.993 1.993 pe7m7|t1 0.670 0.028 24.123 0.000 0.670 0.670 pe7m7|t2 1.839 0.041 44.461 0.000 1.839 1.839 pe7m10|t1 0.657 0.027 24.036 0.000 0.657 0.657 pe7m10|t2 1.947 0.045 43.536 0.000 1.947 1.947 pe7m12|t1 0.723 0.028 26.150 0.000 0.723 0.723 pe7m12|t2 2.013 0.048 42.180 0.000 2.013 2.013 pe11m5|t1 0.746 0.025 30.031 0.000 0.746 0.746 pe11m5|t2 1.840 0.042 43.981 0.000 1.840 1.840 pe11m7|t1 0.895 0.027 33.351 0.000 0.895 0.895 pe11m7|t2 1.926 0.042 46.268 0.000 1.926 1.926 pe11m10|t1 0.830 0.026 31.504 0.000 0.830 0.830 pe11m10|t2 2.015 0.046 43.704 0.000 2.015 2.015 pe11m12|t1 0.899 0.027 32.932 0.000 0.899 0.899 pe11m12|t2 2.033 0.047 43.404 0.000 2.033 2.033 pe13m5|t1 1.594 0.037 43.598 0.000 1.594 1.594 pe13m5|t2 2.667 0.099 26.915 0.000 2.667 2.667 pe13m7|t1 1.446 0.037 39.181 0.000 1.446 1.446 pe13m7|t2 2.594 0.083 31.366 0.000 2.594 2.594 pe13m10|t1 1.240 0.035 35.626 0.000 1.240 1.240 pe13m10|t2 2.378 0.063 38.022 0.000 2.378 2.378 pe13m12|t1 1.185 0.035 33.817 0.000 1.185 1.185 pe13m12|t2 2.270 0.056 40.185 0.000 2.270 2.270 pe25m5|t1 1.181 0.030 39.351 0.000 1.181 1.181 pe25m5|t2 2.292 0.066 34.882 0.000 2.292 2.292 pe25m7|t1 1.365 0.034 39.757 0.000 1.365 1.365 pe25m7|t2 2.303 0.061 37.956 0.000 2.303 2.303 pe25m10|t1 1.348 0.031 42.918 0.000 1.348 1.348 pe25m10|t2 2.313 0.063 36.715 0.000 2.313 2.313 pe25m12|t1 1.235 0.033 37.858 0.000 1.235 1.235 pe25m12|t2 2.437 0.073 33.187 0.000 2.437 2.437

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.208 0.208 0.208 .pe81m7 0.159 0.159 0.159 .pe81m10 0.169 0.169 0.169 .pe81m12 0.109 0.109 0.109 .pe82m5 0.280 0.280 0.280 .pe82m7 0.226 0.226 0.226 .pe82m10 0.237 0.237 0.237 .pe82m12 0.171 0.171 0.171 .pe83m5 0.256 0.256 0.256 .pe83m7 0.206 0.206 0.206 .pe83m10 0.217 0.217 0.217 .pe83m12 0.155 0.155 0.155 .pe86m5 0.338 0.338 0.338 .pe86m7 0.300 0.300 0.300 .pe86m10 0.308 0.308 0.308 .pe86m12 0.261 0.261 0.261 .pe87m5 0.397 0.397 0.397 .pe87m7 0.370 0.370 0.370 .pe87m10 0.376 0.376 0.376 .pe87m12 0.344 0.344 0.344 .pe88m5 0.404 0.404 0.404 .pe88m7 0.366 0.366 0.366 .pe88m10 0.374 0.374 0.374 .pe88m12 0.327 0.327 0.327 .pe89m5 0.381 0.381 0.381 .pe89m7 0.348 0.348 0.348 .pe89m10 0.355 0.355 0.355 .pe89m12 0.314 0.314 0.314 .pe90m5 0.405 0.405 0.405 .pe90m7 0.375 0.375 0.375 .pe90m10 0.382 0.382 0.382 .pe90m12 0.345 0.345 0.345 .pe91m5 0.319 0.319 0.319 .pe91m7 0.283 0.283 0.283 .pe91m10 0.291 0.291 0.291 .pe91m12 0.247 0.247 0.247 .pe2m5 0.424 0.424 0.424 .pe2m7 0.339 0.339 0.339 .pe2m10 0.349 0.349 0.349 .pe2m12 0.300 0.300 0.300 .pe4m5 0.319 0.319 0.319 .pe4m7 0.225 0.225 0.225 .pe4m10 0.237 0.237 0.237 .pe4m12 0.182 0.182 0.182 .pe7m5 0.418 0.418 0.418 .pe7m7 0.340 0.340 0.340 .pe7m10 0.350 0.350 0.350 .pe7m12 0.305 0.305 0.305 .pe11m5 0.372 0.372 0.372 .pe11m7 0.330 0.330 0.330 .pe11m10 0.335 0.335 0.335 .pe11m12 0.311 0.311 0.311 .pe13m5 0.236 0.236 0.236 .pe13m7 0.145 0.145 0.145 .pe13m10 0.156 0.156 0.156 .pe13m12 0.103 0.103 0.103 .pe25m5 0.311 0.311 0.311 .pe25m7 0.228 0.228 0.228 .pe25m10 0.239 0.239 0.239 .pe25m12 0.191 0.191 0.191 RIinat1 0.479 0.028 17.103 0.000 1.000 1.000 RIinat2 0.372 0.031 12.150 0.000 1.000 1.000 RIinat3 0.423 0.028 15.016 0.000 1.000 1.000 RIinat4 0.420 0.025 17.128 0.000 1.000 1.000 RIinat5 0.435 0.021 20.304 0.000 1.000 1.000 RIinat6 0.351 0.026 13.658 0.000 1.000 1.000 RIinat7 0.405 0.025 16.321 0.000 1.000 1.000 RIinat8 0.403 0.023 17.877 0.000 1.000 1.000 RIinat9 0.451 0.027 16.882 0.000 1.000 1.000 RIsi1 0.242 0.066 3.635 0.000 1.000 1.000 RIsi2 0.312 0.069 4.498 0.000 1.000 1.000 RIsi3 0.275 0.058 4.754 0.000 1.000 1.000 RIsi4 0.464 0.037 12.593 0.000 1.000 1.000 RIsi5 0.404 0.074 5.444 0.000 1.000 1.000 RIsi6 0.365 0.065 5.611 0.000 1.000 1.000 WFinat5 0.313 0.028 11.173 0.000 1.000 1.000 .WFinat7 0.286 0.018 16.299 0.000 0.790 0.790 .WFinat10 0.284 0.017 16.775 0.000 0.806 0.806 .WFinat12 0.203 0.014 14.539 0.000 0.492 0.492 WFsi5 0.335 0.069 4.842 0.000 1.000 1.000 .WFsi7 0.273 0.036 7.659 0.000 0.651 0.651 .WFsi10 0.264 0.034 7.734 0.000 0.644 0.644 .WFsi12 0.188 0.028 6.775 0.000 0.410 0.410

S3 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.991 (>0.95) Change in CFI: 0.000 (decrease) Tucker-Lewis Index (TLI) 0.990 (>0.95) Change in TLI: 0.000 (decrease) RMSEA 0.015 (≤ 0.06) Change in RMSEA: 0.000 (increase) 90 Percent confidence interval - lower 0.014 90 Percent confidence interval - upper 0.016
SRMR 0.036 (≤ 0.08) Change in SRMR: 0.000

We attempted to view the fit differences using semTools but it gives the error “a dimention is zero”. We aren’t sure why this is the case, but there is no difference in model fit between S2 and S3, so we can assume that strong invariance holds here.

#summary(semTools::compareFit(RICLPM_multi_inat_S2.fit, RICLPM_multi_inat_S3.fit, nested = TRUE)) #† indicates the best fitting model - have hashed out here

# Table of model fit 
RICLPM_multi_inat_S3.fit.summary.fit <- table.model.fit(model = RICLPM_multi_inat_S3.fit.summary)
# Table of regression coefficients and covariances 
RICLPM_multi_inat_S3.fit.summary.reg <- table.model.coef(model = RICLPM_multi_inat_S3.fit.summary, step = "S3")

Our model does not loose any fit, which means we can assume that strong factorial invariance holds over time. In contrast, If the overall model fit is not significantly worse in the strong invariance model compared to the weak invariance model, it indicates that constraining the item intercepts across time points does not significantly affect the model fit, and strong invariance is supported.

Going forward, we will assume that we have strong invariance for our inattention and social isolation mother report RI-CLPM.

RICLPM_multi_inat_S4: Inattention step 4 - Full model

From Mulder and Hamaker (2021): A significant chi-square difference test would mean strong factorial invariance does not hold, implying that the actual scores cannot be compared over time, but individual differences in scores can still be meaningfully compared since weak factorial invariance holds. As the focus in cross-lagged panel modeling is primarily on comparing individual differences (by decomposing the observed scores into between-unit and withinunit components) rather than mean scores over time, weak factorial invariance may be enough. However, from a measurement point of view, having strong factorial invariance would be considered more ideal.

Instead of including a random intercept at the observed level for each indicator separately (as done so far and in the upper part of the figure at the beginning of this document), we can also choose to specify the entire RI-CLPM at the latent level; this is illustrated in the lower part of the beginning figure. This can be done in either a model with weak or strong factorial invariance over time. To this end, we specify the common factors that capture both trait-like and state-like common variance, and thereby make the assumption that the trait- and state structures coincide. We then decompose these latent variables into a stable, between-unit part and the within-unit components.

We set the factor loadings of these second-order factors to be identical to the corresponding factor loadings of the withinunit factors. Additionally, we constrain the residual variances for the first-order factors to zero. This model is nested under the model we just presented (top panel of the figure), and is statistically equivalent to the model presented in the lower panel of the figure. This implies that we can use a chi-square difference test to compare.

Multiple indicator RI-CLPM, 5 waves with 3 indicators for each variable at each wave (30 observed variables). Fitting a model with constraints to ensure strong factorial invariance, with the RI-CLPM at the latent level.

RICLPM_multi_inat_S4 <- '
  #####################
  # MEASUREMENT MODEL #
  #####################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  Finat5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5
  Finat7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7
  Finat10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10
  Finat12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  Fsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  Fsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  Fsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  Fsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  pe81m5 + pe81m7 + pe81m10 + pe81m12 ~ p*1
  pe82m5 + pe82m7 + pe82m10 + pe82m12 ~ q*1
  pe83m5 + pe83m7 + pe83m10 + pe83m12 ~ r*1
  pe86m5 + pe86m7 + pe86m10 + pe86m12 ~ s*1
  pe87m5 + pe87m7 + pe87m10 + pe87m12 ~ t*1
  pe88m5 + pe88m7 + pe88m10 + pe88m12 ~ u*1
  pe89m5 + pe89m7 + pe89m10 + pe89m12 ~ v*1
  pe90m5 + pe90m7 + pe90m10 + pe90m12 ~ w*1
  pe91m5 + pe91m7 + pe91m10 + pe91m12 ~ x*1
  
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ y*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ z*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ aa*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ ab*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ ac*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ ad*1
  
  # Free latent means from timepoint = 2 (age 7) onward. 
  # Only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  Finat7 + Finat10 + Finat12 + Fsi7 + Fsi10 + Fsi12 ~ 1
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts). 
  RIinat =~ 1*Finat5 + 1*Finat7 + 1*Finat10 + 1*Finat12 
  RIsi =~ 1*Fsi5 + 1*Fsi7 + 1*Fsi10 + 1*Fsi12
  
  # Set the residual variances of all Finat and Fsi variables to 0. 
  Finat5 ~~ 0*Finat5
  Finat7 ~~ 0*Finat7
  Finat10 ~~ 0*Finat10
  Finat12 ~~ 0*Finat12
  Fsi5 ~~ 0*Fsi5
  Fsi7 ~~ 0*Fsi7
  Fsi10 ~~ 0*Fsi10
  Fsi12 ~~ 0*Fsi12

  ###############
  # WITHIN PART #
  ###############
 
  # Create the within-part
  WFinat5 =~ 1*Finat5
  WFinat7 =~ 1*Finat7
  WFinat10 =~ 1*Finat10
  WFinat12 =~ 1*Finat12
  
  WFsi5 =~ 1*Fsi5
  WFsi7 =~ 1*Fsi7
  WFsi10 =~ 1*Fsi10
  WFsi12 =~ 1*Fsi12

  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave - age 5 is missing here. 
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Set correlations between the between-factors (random intercepts) and within-factors at wave 1 at 0. 
  RIinat + RIsi ~~ 0*WFinat5 + 0*WFsi5
'

RICLPM_multi_inat_S4.fit <- cfa(RICLPM_multi_inat_S4, 
                                 data = dat, 
                                 estimator = "WLSMV",
                                 ordered = TRUE,
                                 missing = 'pairwise')

summary(RICLPM_multi_inat_S4.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 107 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 265 Number of equality constraints 84

Number of observations 2232 Number of missing patterns 48

Model Test User Model: Standard Robust Test Statistic 10996.625 7189.732 Degrees of freedom 1709 1709 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.768 Shift parameter 971.165 simple second-order correction

Model Test Baseline Model:

Test statistic 358266.126 88092.703 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.130

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.974 0.937 Tucker-Lewis Index (TLI) 0.973 0.934

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.049 0.038 90 Percent confidence interval - lower 0.048 0.037 90 Percent confidence interval - upper 0.050 0.039 P-value RMSEA <= 0.05 0.885 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.071 0.071

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Finat5 =~
pe81m5 (a) 1.000 0.861 0.861 pe82m5 (b) 0.925 0.008 112.338 0.000 0.797 0.797 pe83m5 (c) 0.959 0.008 124.548 0.000 0.826 0.826 pe86m5 (d) 0.874 0.010 85.720 0.000 0.753 0.753 pe87m5 (e) 0.722 0.015 46.620 0.000 0.622 0.622 pe88m5 (f) 0.777 0.013 62.018 0.000 0.669 0.669 pe89m5 (g) 0.831 0.012 69.405 0.000 0.716 0.716 pe90m5 (h) 0.661 0.016 40.805 0.000 0.569 0.569 pe91m5 (i) 0.842 0.014 61.859 0.000 0.725 0.725 Finat7 =~
pe81m7 (a) 1.000 0.896 0.896 pe82m7 (b) 0.925 0.008 112.338 0.000 0.829 0.829 pe83m7 (c) 0.959 0.008 124.548 0.000 0.859 0.859 pe86m7 (d) 0.874 0.010 85.720 0.000 0.783 0.783 pe87m7 (e) 0.722 0.015 46.620 0.000 0.647 0.647 pe88m7 (f) 0.777 0.013 62.018 0.000 0.696 0.696 pe89m7 (g) 0.831 0.012 69.405 0.000 0.744 0.744 pe90m7 (h) 0.661 0.016 40.805 0.000 0.592 0.592 pe91m7 (i) 0.842 0.014 61.859 0.000 0.754 0.754 Finat10 =~
pe81m10 (a) 1.000 0.890 0.890 pe82m10 (b) 0.925 0.008 112.338 0.000 0.823 0.823 pe83m10 (c) 0.959 0.008 124.548 0.000 0.854 0.854 pe86m10 (d) 0.874 0.010 85.720 0.000 0.777 0.777 pe87m10 (e) 0.722 0.015 46.620 0.000 0.642 0.642 pe88m10 (f) 0.777 0.013 62.018 0.000 0.691 0.691 pe89m10 (g) 0.831 0.012 69.405 0.000 0.739 0.739 pe90m10 (h) 0.661 0.016 40.805 0.000 0.588 0.588 pe91m10 (i) 0.842 0.014 61.859 0.000 0.749 0.749 Finat12 =~
pe81m12 (a) 1.000 0.927 0.927 pe82m12 (b) 0.925 0.008 112.338 0.000 0.858 0.858 pe83m12 (c) 0.959 0.008 124.548 0.000 0.889 0.889 pe86m12 (d) 0.874 0.010 85.720 0.000 0.810 0.810 pe87m12 (e) 0.722 0.015 46.620 0.000 0.669 0.669 pe88m12 (f) 0.777 0.013 62.018 0.000 0.720 0.720 pe89m12 (g) 0.831 0.012 69.405 0.000 0.770 0.770 pe90m12 (h) 0.661 0.016 40.805 0.000 0.613 0.613 pe91m12 (i) 0.842 0.014 61.859 0.000 0.780 0.780 Fsi5 =~
pe2m5 (j) 1.000 0.589 0.589 pe4m5 (k) 1.352 0.048 28.425 0.000 0.797 0.797 pe7m5 (l) 0.908 0.036 25.191 0.000 0.535 0.535 pe11m5 (m) 0.936 0.044 21.354 0.000 0.552 0.552 pe13m5 (n) 1.430 0.050 28.796 0.000 0.843 0.843 pe25m5 (o) 1.311 0.050 26.129 0.000 0.772 0.772 Fsi7 =~
pe2m7 (j) 1.000 0.635 0.635 pe4m7 (k) 1.352 0.048 28.425 0.000 0.858 0.858 pe7m7 (l) 0.908 0.036 25.191 0.000 0.576 0.576 pe11m7 (m) 0.936 0.044 21.354 0.000 0.594 0.594 pe13m7 (n) 1.430 0.050 28.796 0.000 0.907 0.907 pe25m7 (o) 1.311 0.050 26.129 0.000 0.832 0.832 Fsi10 =~
pe2m10 (j) 1.000 0.630 0.630 pe4m10 (k) 1.352 0.048 28.425 0.000 0.853 0.853 pe7m10 (l) 0.908 0.036 25.191 0.000 0.573 0.573 pe11m10 (m) 0.936 0.044 21.354 0.000 0.590 0.590 pe13m10 (n) 1.430 0.050 28.796 0.000 0.901 0.901 pe25m10 (o) 1.311 0.050 26.129 0.000 0.826 0.826 Fsi12 =~
pe2m12 (j) 1.000 0.650 0.650 pe4m12 (k) 1.352 0.048 28.425 0.000 0.879 0.879 pe7m12 (l) 0.908 0.036 25.191 0.000 0.590 0.590 pe11m12 (m) 0.936 0.044 21.354 0.000 0.609 0.609 pe13m12 (n) 1.430 0.050 28.796 0.000 0.930 0.930 pe25m12 (o) 1.311 0.050 26.129 0.000 0.852 0.852 RIinat =~
Finat5 1.000 0.716 0.716 Finat7 1.000 0.689 0.689 Finat10 1.000 0.694 0.694 Finat12 1.000 0.666 0.666 RIsi =~
Fsi5 1.000 NA NA Fsi7 1.000 NA NA Fsi10 1.000 NA NA Fsi12 1.000 NA NA WFinat5 =~
Finat5 1.000 0.698 0.698 WFinat7 =~
Finat7 1.000 0.725 0.725 WFinat10 =~
Finat10 1.000 0.720 0.720 WFinat12 =~
Finat12 1.000 0.746 0.746 WFsi5 =~
Fsi5 1.000 1.001 1.001 WFsi7 =~
Fsi7 1.000 1.001 1.001 WFsi10 =~
Fsi10 1.000 1.001 1.001 WFsi12 =~
Fsi12 1.000 1.001 1.001

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.542 0.054 9.964 0.000 0.502 0.502 WFsi5 -0.110 0.083 -1.331 0.183 -0.100 -0.100 WFsi7 ~
WFinat5 -0.075 0.052 -1.439 0.150 -0.071 -0.071 WFsi5 0.860 0.064 13.550 0.000 0.799 0.799 WFinat10 ~
WFinat7 0.474 0.056 8.493 0.000 0.480 0.480 WFsi7 -0.058 0.089 -0.655 0.512 -0.058 -0.058 WFsi10 ~
WFinat7 -0.026 0.052 -0.512 0.609 -0.027 -0.027 WFsi7 0.751 0.093 8.042 0.000 0.756 0.756 WFinat12 ~
WFinat10 0.817 0.029 28.149 0.000 0.757 0.757 WFsi10 -0.042 0.044 -0.965 0.334 -0.039 -0.039 WFsi12 ~
WFinat10 0.045 0.036 1.243 0.214 0.044 0.044 WFsi10 0.900 0.043 21.063 0.000 0.873 0.873

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .WFinat7 ~~
.WFsi7 0.094 0.013 6.984 0.000 0.433 0.433 .WFinat10 ~~
.WFsi10 0.062 0.012 4.990 0.000 0.265 0.265 .WFinat12 ~~
.WFsi12 0.066 0.010 6.674 0.000 0.471 0.471 RIinat ~~
WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIsi ~~
WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIinat ~~
RIsi 0.201 0.052 3.842 0.000 14.519 14.519 WFinat5 ~~
WFsi5 0.038 0.053 0.720 0.472 0.108 0.108

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 (p) -0.012 0.010 -1.204 0.229 -0.012 -0.012 .pe81m7 (p) -0.012 0.010 -1.204 0.229 -0.012 -0.012 .pe81m10 (p) -0.012 0.010 -1.204 0.229 -0.012 -0.012 .pe81m12 (p) -0.012 0.010 -1.204 0.229 -0.012 -0.012 .pe82m5 (q) -0.017 0.009 -1.855 0.064 -0.017 -0.017 .pe82m7 (q) -0.017 0.009 -1.855 0.064 -0.017 -0.017 .pe82m10 (q) -0.017 0.009 -1.855 0.064 -0.017 -0.017 .pe82m12 (q) -0.017 0.009 -1.855 0.064 -0.017 -0.017 .pe83m5 (r) -0.002 0.009 -0.267 0.789 -0.002 -0.002 .pe83m7 (r) -0.002 0.009 -0.267 0.789 -0.002 -0.002 .pe83m10 (r) -0.002 0.009 -0.267 0.789 -0.002 -0.002 .pe83m12 (r) -0.002 0.009 -0.267 0.789 -0.002 -0.002 .pe86m5 (s) 0.025 0.009 2.618 0.009 0.025 0.025 .pe86m7 (s) 0.025 0.009 2.618 0.009 0.025 0.025 .pe86m10 (s) 0.025 0.009 2.618 0.009 0.025 0.025 .pe86m12 (s) 0.025 0.009 2.618 0.009 0.025 0.025 .pe87m5 (t) -0.013 0.012 -1.092 0.275 -0.013 -0.013 .pe87m7 (t) -0.013 0.012 -1.092 0.275 -0.013 -0.013 .pe87m10 (t) -0.013 0.012 -1.092 0.275 -0.013 -0.013 .pe87m12 (t) -0.013 0.012 -1.092 0.275 -0.013 -0.013 .pe88m5 (u) -0.010 0.010 -1.074 0.283 -0.010 -0.010 .pe88m7 (u) -0.010 0.010 -1.074 0.283 -0.010 -0.010 .pe88m10 (u) -0.010 0.010 -1.074 0.283 -0.010 -0.010 .pe88m12 (u) -0.010 0.010 -1.074 0.283 -0.010 -0.010 .pe89m5 (v) -0.028 0.011 -2.698 0.007 -0.028 -0.028 .pe89m7 (v) -0.028 0.011 -2.698 0.007 -0.028 -0.028 .pe89m10 (v) -0.028 0.011 -2.698 0.007 -0.028 -0.028 .pe89m12 (v) -0.028 0.011 -2.698 0.007 -0.028 -0.028 .pe90m5 (w) -0.003 0.010 -0.333 0.739 -0.003 -0.003 .pe90m7 (w) -0.003 0.010 -0.333 0.739 -0.003 -0.003 .pe90m10 (w) -0.003 0.010 -0.333 0.739 -0.003 -0.003 .pe90m12 (w) -0.003 0.010 -0.333 0.739 -0.003 -0.003 .pe91m5 (x) -0.008 0.011 -0.720 0.471 -0.008 -0.008 .pe91m7 (x) -0.008 0.011 -0.720 0.471 -0.008 -0.008 .pe91m10 (x) -0.008 0.011 -0.720 0.471 -0.008 -0.008 .pe91m12 (x) -0.008 0.011 -0.720 0.471 -0.008 -0.008 .pe2m5 (y) 0.065 0.016 4.137 0.000 0.065 0.065 .pe2m7 (y) 0.065 0.016 4.137 0.000 0.065 0.065 .pe2m10 (y) 0.065 0.016 4.137 0.000 0.065 0.065 .pe2m12 (y) 0.065 0.016 4.137 0.000 0.065 0.065 .pe4m5 (z) 0.068 0.014 4.840 0.000 0.068 0.068 .pe4m7 (z) 0.068 0.014 4.840 0.000 0.068 0.068 .pe4m10 (z) 0.068 0.014 4.840 0.000 0.068 0.068 .pe4m12 (z) 0.068 0.014 4.840 0.000 0.068 0.068 .pe7m5 (aa) 0.046 0.014 3.261 0.001 0.046 0.046 .pe7m7 (aa) 0.046 0.014 3.261 0.001 0.046 0.046 .pe7m10 (aa) 0.046 0.014 3.261 0.001 0.046 0.046 .pe7m12 (aa) 0.046 0.014 3.261 0.001 0.046 0.046 .pe11m5 (ab) 0.060 0.015 3.993 0.000 0.060 0.060 .pe11m7 (ab) 0.060 0.015 3.993 0.000 0.060 0.060 .pe11m10 (ab) 0.060 0.015 3.993 0.000 0.060 0.060 .pe11m12 (ab) 0.060 0.015 3.993 0.000 0.060 0.060 .pe13m5 (ac) 0.023 0.020 1.130 0.259 0.023 0.023 .pe13m7 (ac) 0.023 0.020 1.130 0.259 0.023 0.023 .pe13m10 (ac) 0.023 0.020 1.130 0.259 0.023 0.023 .pe13m12 (ac) 0.023 0.020 1.130 0.259 0.023 0.023 .pe25m5 (ad) 0.015 0.018 0.841 0.400 0.015 0.015 .pe25m7 (ad) 0.015 0.018 0.841 0.400 0.015 0.015 .pe25m10 (ad) 0.015 0.018 0.841 0.400 0.015 0.015 .pe25m12 (ad) 0.015 0.018 0.841 0.400 0.015 0.015 .Finat7 0.008 0.017 0.460 0.646 0.009 0.009 .Finat10 0.008 0.018 0.440 0.660 0.009 0.009 .Finat12 0.008 0.019 0.423 0.672 0.009 0.009 .Fsi7 -0.037 0.021 -1.739 0.082 -0.059 -0.059 .Fsi10 -0.037 0.021 -1.762 0.078 -0.059 -0.059 .Fsi12 -0.036 0.023 -1.578 0.115 -0.055 -0.055 .Finat5 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 RIinat 0.000 0.000 0.000 RIsi 0.000 NA NA WFinat5 0.000 0.000 0.000 .WFinat7 0.000 0.000 0.000 .WFinat10 0.000 0.000 0.000 .WFinat12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5|t1 0.247 0.021 11.528 0.000 0.247 0.247 pe81m5|t2 1.085 0.027 40.060 0.000 1.085 1.085 pe82m5|t1 0.080 0.022 3.694 0.000 0.080 0.080 pe82m5|t2 1.053 0.027 38.983 0.000 1.053 1.053 pe83m5|t1 -0.113 0.021 -5.273 0.000 -0.113 -0.113 pe83m5|t2 0.760 0.024 31.759 0.000 0.760 0.760 pe86m5|t1 0.087 0.022 3.940 0.000 0.087 0.087 pe86m5|t2 1.164 0.028 41.044 0.000 1.164 1.164 pe87m5|t1 0.303 0.023 13.162 0.000 0.303 0.303 pe87m5|t2 1.483 0.035 42.196 0.000 1.483 1.483 pe88m5|t1 0.393 0.023 17.368 0.000 0.393 0.393 pe88m5|t2 1.128 0.029 39.180 0.000 1.128 1.128 pe89m5|t1 0.450 0.023 19.677 0.000 0.450 0.450 pe89m5|t2 1.262 0.031 41.204 0.000 1.262 1.262 pe90m5|t1 0.051 0.022 2.363 0.018 0.051 0.051 pe90m5|t2 0.803 0.025 32.641 0.000 0.803 0.803 pe91m5|t1 0.571 0.023 24.697 0.000 0.571 0.571 pe91m5|t2 1.435 0.033 43.401 0.000 1.435 1.435 pe81m7|t1 0.302 0.019 15.612 0.000 0.302 0.302 pe81m7|t2 1.178 0.023 51.639 0.000 1.178 1.178 pe82m7|t1 0.184 0.020 9.060 0.000 0.184 0.184 pe82m7|t2 1.247 0.025 50.403 0.000 1.247 1.247 pe83m7|t1 -0.020 0.020 -1.002 0.316 -0.020 -0.020 pe83m7|t2 0.968 0.021 46.340 0.000 0.968 0.968 pe86m7|t1 0.178 0.021 8.651 0.000 0.178 0.178 pe86m7|t2 1.339 0.026 50.536 0.000 1.339 1.339 pe87m7|t1 0.259 0.022 11.559 0.000 0.259 0.259 pe87m7|t2 1.632 0.036 44.867 0.000 1.632 1.632 pe88m7|t1 0.323 0.021 15.431 0.000 0.323 0.323 pe88m7|t2 1.278 0.027 46.670 0.000 1.278 1.278 pe89m7|t1 0.476 0.021 23.137 0.000 0.476 0.476 pe89m7|t2 1.386 0.029 47.065 0.000 1.386 1.386 pe90m7|t1 0.047 0.021 2.197 0.028 0.047 0.047 pe90m7|t2 0.989 0.024 41.521 0.000 0.989 0.989 pe91m7|t1 0.569 0.021 26.726 0.000 0.569 0.569 pe91m7|t2 1.532 0.030 50.421 0.000 1.532 1.532 pe81m10|t1 0.388 0.019 20.717 0.000 0.388 0.388 pe81m10|t2 1.210 0.023 52.329 0.000 1.210 1.210 pe82m10|t1 0.216 0.021 10.509 0.000 0.216 0.216 pe82m10|t2 1.317 0.026 50.980 0.000 1.317 1.317 pe83m10|t1 -0.020 0.020 -1.022 0.307 -0.020 -0.020 pe83m10|t2 1.017 0.022 47.144 0.000 1.017 1.017 pe86m10|t1 0.186 0.021 9.067 0.000 0.186 0.186 pe86m10|t2 1.317 0.027 49.394 0.000 1.317 1.317 pe87m10|t1 0.398 0.023 17.671 0.000 0.398 0.398 pe87m10|t2 1.757 0.040 44.158 0.000 1.757 1.757 pe88m10|t1 0.051 0.021 2.491 0.013 0.051 0.051 pe88m10|t2 1.009 0.023 43.225 0.000 1.009 1.009 pe89m10|t1 0.442 0.021 20.994 0.000 0.442 0.442 pe89m10|t2 1.377 0.029 46.715 0.000 1.377 1.377 pe90m10|t1 0.043 0.021 2.074 0.038 0.043 0.043 pe90m10|t2 0.940 0.024 39.951 0.000 0.940 0.940 pe91m10|t1 0.646 0.022 29.777 0.000 0.646 0.646 pe91m10|t2 1.603 0.034 47.836 0.000 1.603 1.603 pe81m12|t1 0.423 0.019 22.423 0.000 0.423 0.423 pe81m12|t2 1.247 0.023 53.598 0.000 1.247 1.247 pe82m12|t1 0.297 0.020 15.036 0.000 0.297 0.297 pe82m12|t2 1.359 0.026 51.988 0.000 1.359 1.359 pe83m12|t1 0.056 0.020 2.832 0.005 0.056 0.056 pe83m12|t2 1.102 0.022 49.498 0.000 1.102 1.102 pe86m12|t1 0.111 0.021 5.353 0.000 0.111 0.111 pe86m12|t2 1.372 0.027 50.431 0.000 1.372 1.372 pe87m12|t1 0.358 0.022 16.464 0.000 0.358 0.358 pe87m12|t2 1.690 0.038 44.719 0.000 1.690 1.690 pe88m12|t1 0.006 0.021 0.275 0.783 0.006 0.006 pe88m12|t2 0.938 0.023 41.606 0.000 0.938 0.938 pe89m12|t1 0.439 0.021 20.794 0.000 0.439 0.439 pe89m12|t2 1.362 0.028 47.984 0.000 1.362 1.362 pe90m12|t1 0.080 0.021 3.878 0.000 0.080 0.080 pe90m12|t2 0.971 0.024 41.103 0.000 0.971 0.971 pe91m12|t1 0.571 0.021 27.229 0.000 0.571 0.571 pe91m12|t2 1.621 0.034 47.471 0.000 1.621 1.621 pe2m5|t1 1.430 0.033 43.171 0.000 1.430 1.430 pe2m5|t2 2.432 0.074 32.816 0.000 2.432 2.432 pe4m5|t1 1.076 0.027 39.889 0.000 1.076 1.076 pe4m5|t2 2.198 0.057 38.709 0.000 2.198 2.198 pe7m5|t1 0.880 0.026 33.686 0.000 0.880 0.880 pe7m5|t2 2.004 0.050 40.308 0.000 2.004 2.004 pe11m5|t1 0.749 0.025 30.086 0.000 0.749 0.749 pe11m5|t2 1.844 0.042 44.049 0.000 1.844 1.844 pe13m5|t1 1.599 0.037 43.785 0.000 1.599 1.599 pe13m5|t2 2.672 0.099 27.082 0.000 2.672 2.672 pe25m5|t1 1.187 0.030 39.564 0.000 1.187 1.187 pe25m5|t2 2.298 0.066 35.078 0.000 2.298 2.298 pe2m7|t1 1.203 0.032 37.354 0.000 1.203 1.203 pe2m7|t2 2.286 0.059 38.547 0.000 2.286 2.286 pe4m7|t1 1.030 0.032 31.763 0.000 1.030 1.030 pe4m7|t2 2.277 0.053 42.602 0.000 2.277 2.277 pe7m7|t1 0.674 0.027 25.000 0.000 0.674 0.674 pe7m7|t2 1.843 0.042 43.601 0.000 1.843 1.843 pe11m7|t1 0.884 0.027 32.139 0.000 0.884 0.884 pe11m7|t2 1.915 0.041 46.259 0.000 1.915 1.915 pe13m7|t1 1.427 0.038 37.634 0.000 1.427 1.427 pe13m7|t2 2.575 0.081 31.937 0.000 2.575 2.575 pe25m7|t1 1.350 0.035 38.330 0.000 1.350 1.350 pe25m7|t2 2.289 0.059 38.579 0.000 2.289 2.289 pe2m10|t1 1.034 0.030 34.258 0.000 1.034 1.034 pe2m10|t2 2.163 0.054 40.232 0.000 2.163 2.163 pe4m10|t1 0.935 0.031 30.344 0.000 0.935 0.935 pe4m10|t2 2.165 0.046 46.762 0.000 2.165 2.165 pe7m10|t1 0.662 0.026 25.030 0.000 0.662 0.662 pe7m10|t2 1.952 0.046 42.578 0.000 1.952 1.952 pe11m10|t1 0.820 0.027 30.543 0.000 0.820 0.820 pe11m10|t2 2.005 0.046 43.922 0.000 2.005 2.005 pe13m10|t1 1.222 0.036 34.336 0.000 1.222 1.222 pe13m10|t2 2.360 0.061 38.761 0.000 2.360 2.360 pe25m10|t1 1.335 0.032 42.320 0.000 1.335 1.335 pe25m10|t2 2.299 0.062 37.257 0.000 2.299 2.299 pe2m12|t1 1.158 0.030 38.076 0.000 1.158 1.158 pe2m12|t2 2.267 0.059 38.667 0.000 2.267 2.267 pe4m12|t1 0.922 0.033 28.046 0.000 0.922 0.922 pe4m12|t2 2.244 0.051 44.213 0.000 2.244 2.244 pe7m12|t1 0.730 0.026 27.732 0.000 0.730 0.730 pe7m12|t2 2.020 0.049 41.005 0.000 2.020 2.020 pe11m12|t1 0.890 0.028 32.194 0.000 0.890 0.890 pe11m12|t2 2.023 0.046 43.641 0.000 2.023 2.023 pe13m12|t1 1.170 0.036 32.719 0.000 1.170 1.170 pe13m12|t2 2.255 0.055 41.057 0.000 2.255 2.255 pe25m12|t1 1.224 0.033 37.171 0.000 1.224 1.224 pe25m12|t2 2.425 0.072 33.546 0.000 2.425 2.425

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Finat5 0.000 0.000 0.000 .Finat7 0.000 0.000 0.000 .Finat10 0.000 0.000 0.000 .Finat12 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 .Fsi7 0.000 0.000 0.000 .Fsi10 0.000 0.000 0.000 .Fsi12 0.000 0.000 0.000 .pe81m5 0.258 0.258 0.258 .pe82m5 0.365 0.365 0.365 .pe83m5 0.317 0.317 0.317 .pe86m5 0.434 0.434 0.434 .pe87m5 0.613 0.613 0.613 .pe88m5 0.552 0.552 0.552 .pe89m5 0.488 0.488 0.488 .pe90m5 0.676 0.676 0.676 .pe91m5 0.474 0.474 0.474 .pe81m7 0.198 0.198 0.198 .pe82m7 0.313 0.313 0.313 .pe83m7 0.262 0.262 0.262 .pe86m7 0.388 0.388 0.388 .pe87m7 0.582 0.582 0.582 .pe88m7 0.516 0.516 0.516 .pe89m7 0.446 0.446 0.446 .pe90m7 0.650 0.650 0.650 .pe91m7 0.432 0.432 0.432 .pe81m10 0.208 0.208 0.208 .pe82m10 0.322 0.322 0.322 .pe83m10 0.271 0.271 0.271 .pe86m10 0.396 0.396 0.396 .pe87m10 0.587 0.587 0.587 .pe88m10 0.522 0.522 0.522 .pe89m10 0.453 0.453 0.453 .pe90m10 0.654 0.654 0.654 .pe91m10 0.439 0.439 0.439 .pe81m12 0.141 0.141 0.141 .pe82m12 0.264 0.264 0.264 .pe83m12 0.209 0.209 0.209 .pe86m12 0.344 0.344 0.344 .pe87m12 0.552 0.552 0.552 .pe88m12 0.481 0.481 0.481 .pe89m12 0.407 0.407 0.407 .pe90m12 0.625 0.625 0.625 .pe91m12 0.391 0.391 0.391 .pe2m5 0.653 0.653 0.653 .pe4m5 0.365 0.365 0.365 .pe7m5 0.714 0.714 0.714 .pe11m5 0.696 0.696 0.696 .pe13m5 0.290 0.290 0.290 .pe25m5 0.403 0.403 0.403 .pe2m7 0.597 0.597 0.597 .pe4m7 0.263 0.263 0.263 .pe7m7 0.668 0.668 0.668 .pe11m7 0.647 0.647 0.647 .pe13m7 0.177 0.177 0.177 .pe25m7 0.308 0.308 0.308 .pe2m10 0.602 0.602 0.602 .pe4m10 0.273 0.273 0.273 .pe7m10 0.672 0.672 0.672 .pe11m10 0.652 0.652 0.652 .pe13m10 0.187 0.187 0.187 .pe25m10 0.317 0.317 0.317 .pe2m12 0.577 0.577 0.577 .pe4m12 0.227 0.227 0.227 .pe7m12 0.651 0.651 0.651 .pe11m12 0.629 0.629 0.629 .pe13m12 0.136 0.136 0.136 .pe25m12 0.274 0.274 0.274 RIinat 0.381 0.034 11.065 0.000 1.000 1.000 RIsi -0.001 0.127 -0.004 0.997 NA NA WFinat5 0.361 0.035 10.400 0.000 1.000 1.000 .WFinat7 0.316 0.015 20.585 0.000 0.749 0.749 .WFinat10 0.318 0.015 21.661 0.000 0.775 0.775 .WFinat12 0.208 0.013 15.639 0.000 0.434 0.434 WFsi5 0.348 0.129 2.692 0.007 1.000 1.000 .WFsi7 0.149 0.018 8.291 0.000 0.369 0.369 .WFsi10 0.173 0.017 10.238 0.000 0.434 0.434 .WFsi12 0.095 0.013 7.172 0.000 0.224 0.224

S4 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S3 model) Comparative Fit Index (CFI) 0.937 (>0.95) Change in CFI: 0.056 (decrease) - worse fit Tucker-Lewis Index (TLI) 0.934 (>0.95) Change in TLI: 0.057 (decrease) - worse fit RMSEA 0.038 (≤ 0.06) Change in RMSEA: 0.024 (increase) - worse fit 90 Percent confidence interval - lower 0.037 90 Percent confidence interval - upper 0.049
SRMR 0.071 (≤ 0.08) Change in SRMR: 0.036 (increase) - worse fit

summary(semTools::compareFit(RICLPM_multi_inat_S3.fit, RICLPM_multi_inat_S4.fit, nested = TRUE)) #† indicates the best fitting model

Nested Model Comparison

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

                       Df AIC BIC   Chisq Chisq diff Df diff Pr(>Chisq)

RICLPM_multi_inat_S3.fit 1592 2267.9
RICLPM_multi_inat_S4.fit 1709 10996.6 2004.6 117 < 2.2e-16 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Model Fit Indices

                     chisq.scaled df.scaled pvalue.scaled rmsea.scaled

RICLPM_multi_inat_S3.fit 2359.454† 1592 .000 .015† RICLPM_multi_inat_S4.fit 7189.732 1709 .000 .038 cfi.scaled tli.scaled srmr RICLPM_multi_inat_S3.fit .991† .990† .035† RICLPM_multi_inat_S4.fit .937 .934 .071

Differences in Fit Indices

                                                df.scaled rmsea.scaled

RICLPM_multi_inat_S4.fit - RICLPM_multi_inat_S3.fit 117 0.023 cfi.scaled tli.scaled srmr RICLPM_multi_inat_S4.fit - RICLPM_multi_inat_S3.fit -0.055 -0.056 0.036

We can conclude that S3 is the best fitting model for inattention scores and social isolation, mother report

The nonsignificant chi square implies that the current model does not have to be rejected, and we can say that there is measurement invariance across the stable between structure and fluctuating within-structure. If the chi-square test is significant, then we need to conclude that these structures do not coincide, and temporal fluctuations within individuals take place on a different underlying dimension than the stable differences between units (see Hamaker et al. (2017) for further discussion on this).

From Hamaker et al. (2017): need to add notes here

To key points from Hamaker et al., 2015 (page 646 & 647) 1. The disadvantage of using sum and mean scores however is that one assumes an absence of measurement error, which often is an unrealistic assumption, especially within the social sciences (Griliches & Hausman, 1986). Failing to properly account for measurement error can bias lagged-parameter estimates downward, leading to a loss of power. Also, the estimation of factor scores is difficult due to the problem of factor indeterminacy (i.e., there are multiple ways to obtain factor scores, each with their own set of advantages and disadvantages), and it is unclear how this affects the results of the RI-CLPM. 2. The procedure described above for establishing measurement invariance relies heavily on chi-square difference testing which, as mentioned before, can have serious disadvantages such as an increased Type I and Type II error rate when the base model is misspecified (Yuan & Bentler, 2004). Alternatively, researchers can use equivalence testing (Yuan & Chan, 2016), which allows researchers to explicitly specify an acceptable level of model misfit.

Multiple indicator RI-CLPM: Hyperactivity and social isolation

RICLPM_multi_hyp_S1: Hyperactivity/Implsivity step 1

Multiple response items RICLPM mother report hyperactivity ADHD symptoms and social isolation: Step 1, the configural model (S1)

RICLPM_multi_hyp_S1 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyperactivity (mother report)
  RIhyp1 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIhyp2 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIhyp3 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIhyp4 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIhyp5 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIhyp6 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIhyp7 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIhyp8 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIhyp9 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyperactivity symptoms at 4 waves
  WFhyp5 =~ pe84m5 + pe85m5 + pe96m5 + pe97m5 + pe92m5 + pe93m5 + pe94m5 + pe95m5 + pe64m5
  WFhyp7 =~ pe84m7 + pe85m7 + pe96m7 + pe97m7 + pe92m7 + pe93m7 + pe94m7 + pe95m7 + pe64m7
  WFhyp10 =~ pe84m10 + pe85m10 + pe96m10 + pe97m10 + pe92m10 + pe93m10 + pe94m10 + pe95m10 + pe64m10
  WFhyp12 =~ pe84m12 + pe85m12 + pe96m12 + pe97m12 + pe92m12 + pe93m12 + pe94m12 + pe95m12 + pe64m12 
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ pe2m5 + pe4m5 + pe7m5 + pe11m5 + pe13m5 + pe25m5 
  WFsi7 =~ pe2m7 + pe4m7 + pe7m7 + pe11m7 + pe13m7 + pe25m7 
  WFsi10 =~ pe2m10 + pe4m10 + pe7m10 + pe11m10 + pe13m10 + pe25m10
  WFsi12 =~ pe2m12 + pe4m12 + pe7m12 + pe11m12 + pe13m12 + pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPM_multi_hyp_S1.fit <- cfa(RICLPM_multi_hyp_S1, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,         
                           missing = 'pairwise'  
)

summary(RICLPM_multi_hyp_S1.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 188 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316

Number of observations 2232 Number of missing patterns 43

Model Test User Model: Standard Robust Test Statistic 2072.179 2559.537 Degrees of freedom 1574 1574 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.169 Shift parameter 786.670 simple second-order correction

Model Test Baseline Model:

Test statistic 361279.045 95505.953 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 3.835

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.999 0.989 Tucker-Lewis Index (TLI) 0.998 0.988

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.012 0.017 90 Percent confidence interval - lower 0.010 0.016 90 Percent confidence interval - upper 0.013 0.018 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.033 0.033

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIhyp1 =~
pe84m5 1.000 0.640 0.640 pe84m7 1.000 0.640 0.640 pe84m10 1.000 0.640 0.640 pe84m12 1.000 0.640 0.640 RIhyp2 =~
pe85m5 1.000 0.594 0.594 pe85m7 1.000 0.594 0.594 pe85m10 1.000 0.594 0.594 pe85m12 1.000 0.594 0.594 RIhyp3 =~
pe96m5 1.000 0.599 0.599 pe96m7 1.000 0.599 0.599 pe96m10 1.000 0.599 0.599 pe96m12 1.000 0.599 0.599 RIhyp4 =~
pe97m5 1.000 0.573 0.573 pe97m7 1.000 0.573 0.573 pe97m10 1.000 0.573 0.573 pe97m12 1.000 0.573 0.573 RIhyp5 =~
pe92m5 1.000 0.633 0.633 pe92m7 1.000 0.633 0.633 pe92m10 1.000 0.633 0.633 pe92m12 1.000 0.633 0.633 RIhyp6 =~
pe93m5 1.000 0.620 0.620 pe93m7 1.000 0.620 0.620 pe93m10 1.000 0.620 0.620 pe93m12 1.000 0.620 0.620 RIhyp7 =~
pe94m5 1.000 0.619 0.619 pe94m7 1.000 0.619 0.619 pe94m10 1.000 0.619 0.619 pe94m12 1.000 0.619 0.619 RIhyp8 =~
pe95m5 1.000 0.668 0.668 pe95m7 1.000 0.668 0.668 pe95m10 1.000 0.668 0.668 pe95m12 1.000 0.668 0.668 RIhyp9 =~
pe64m5 1.000 0.747 0.747 pe64m7 1.000 0.747 0.747 pe64m10 1.000 0.747 0.747 pe64m12 1.000 0.747 0.747 RIsi1 =~
pe2m5 1.000 0.472 0.472 pe2m7 1.000 0.472 0.472 pe2m10 1.000 0.472 0.472 pe2m12 1.000 0.472 0.472 RIsi2 =~
pe4m5 1.000 0.328 0.328 pe4m7 1.000 0.328 0.328 pe4m10 1.000 0.328 0.328 pe4m12 1.000 0.328 0.328 RIsi3 =~
pe7m5 1.000 0.511 0.511 pe7m7 1.000 0.511 0.511 pe7m10 1.000 0.511 0.511 pe7m12 1.000 0.511 0.511 RIsi4 =~
pe11m5 1.000 0.642 0.642 pe11m7 1.000 0.642 0.642 pe11m10 1.000 0.642 0.642 pe11m12 1.000 0.642 0.642 RIsi5 =~
pe13m5 1.000 0.489 0.489 pe13m7 1.000 0.489 0.489 pe13m10 1.000 0.489 0.489 pe13m12 1.000 0.489 0.489 RIsi6 =~
pe25m5 1.000 0.494 0.494 pe25m7 1.000 0.494 0.494 pe25m10 1.000 0.494 0.494 pe25m12 1.000 0.494 0.494 WFhyp5 =~
pe84m5 1.000 0.444 0.444 pe85m5 1.067 0.082 12.991 0.000 0.474 0.474 pe96m5 0.595 0.091 6.532 0.000 0.264 0.264 pe97m5 1.183 0.089 13.245 0.000 0.525 0.525 pe92m5 1.334 0.092 14.557 0.000 0.593 0.593 pe93m5 1.546 0.105 14.690 0.000 0.687 0.687 pe94m5 1.457 0.104 14.010 0.000 0.647 0.647 pe95m5 1.235 0.094 13.078 0.000 0.548 0.548 pe64m5 0.684 0.076 8.995 0.000 0.304 0.304 WFhyp7 =~
pe84m7 1.000 0.495 0.495 pe85m7 1.039 0.070 14.843 0.000 0.514 0.514 pe96m7 0.885 0.074 12.031 0.000 0.438 0.438 pe97m7 1.105 0.075 14.810 0.000 0.547 0.547 pe92m7 1.252 0.076 16.579 0.000 0.620 0.620 pe93m7 1.379 0.082 16.792 0.000 0.682 0.682 pe94m7 1.337 0.084 15.857 0.000 0.662 0.662 pe95m7 1.158 0.077 15.041 0.000 0.573 0.573 pe64m7 0.500 0.068 7.358 0.000 0.247 0.247 WFhyp10 =~
pe84m10 1.000 0.561 0.561 pe85m10 1.093 0.051 21.337 0.000 0.614 0.614 pe96m10 1.047 0.059 17.833 0.000 0.588 0.588 pe97m10 1.138 0.057 19.912 0.000 0.639 0.639 pe92m10 1.150 0.057 20.248 0.000 0.646 0.646 pe93m10 1.198 0.059 20.332 0.000 0.673 0.673 pe94m10 1.141 0.063 17.982 0.000 0.640 0.640 pe95m10 0.914 0.055 16.500 0.000 0.513 0.513 pe64m10 0.687 0.053 13.060 0.000 0.386 0.386 WFhyp12 =~
pe84m12 1.000 0.578 0.578 pe85m12 1.085 0.047 23.290 0.000 0.627 0.627 pe96m12 1.175 0.059 19.899 0.000 0.678 0.678 pe97m12 1.181 0.052 22.926 0.000 0.682 0.682 pe92m12 1.128 0.048 23.357 0.000 0.652 0.652 pe93m12 1.228 0.052 23.543 0.000 0.709 0.709 pe94m12 1.179 0.054 21.670 0.000 0.681 0.681 pe95m12 0.954 0.049 19.403 0.000 0.551 0.551 pe64m12 0.735 0.050 14.753 0.000 0.425 0.425 WFsi5 =~
pe2m5 1.000 0.551 0.551 pe4m5 1.451 0.170 8.547 0.000 0.799 0.799 pe7m5 0.764 0.110 6.977 0.000 0.421 0.421 pe11m5 0.925 0.127 7.304 0.000 0.509 0.509 pe13m5 1.337 0.169 7.904 0.000 0.736 0.736 pe25m5 1.272 0.151 8.438 0.000 0.700 0.700 WFsi7 =~
pe2m7 1.000 0.585 0.585 pe4m7 1.459 0.135 10.795 0.000 0.853 0.853 pe7m7 0.953 0.102 9.311 0.000 0.557 0.557 pe11m7 0.867 0.100 8.676 0.000 0.507 0.507 pe13m7 1.379 0.135 10.203 0.000 0.806 0.806 pe25m7 1.240 0.126 9.806 0.000 0.725 0.725 WFsi10 =~
pe2m10 1.000 0.608 0.608 pe4m10 1.356 0.110 12.356 0.000 0.825 0.825 pe7m10 0.996 0.091 10.959 0.000 0.605 0.605 pe11m10 0.723 0.084 8.560 0.000 0.440 0.440 pe13m10 1.324 0.114 11.649 0.000 0.805 0.805 pe25m10 1.185 0.109 10.820 0.000 0.720 0.720 WFsi12 =~
pe2m12 1.000 0.683 0.683 pe4m12 1.238 0.083 14.955 0.000 0.846 0.846 pe7m12 1.008 0.073 13.789 0.000 0.689 0.689 pe11m12 0.797 0.072 11.061 0.000 0.545 0.545 pe13m12 1.161 0.084 13.837 0.000 0.793 0.793 pe25m12 1.066 0.083 12.773 0.000 0.729 0.729

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.565 0.070 8.109 0.000 0.506 0.506 WFsi5 0.159 0.057 2.802 0.005 0.177 0.177 WFsi7 ~
WFhyp5 0.223 0.085 2.635 0.008 0.169 0.169 WFsi5 0.637 0.101 6.320 0.000 0.600 0.600 WFhyp10 ~
WFhyp7 0.591 0.060 9.814 0.000 0.521 0.521 WFsi7 0.188 0.055 3.434 0.001 0.196 0.196 WFsi10 ~
WFhyp7 0.285 0.073 3.887 0.000 0.232 0.232 WFsi7 0.592 0.083 7.095 0.000 0.570 0.570 WFhyp12 ~
WFhyp10 0.726 0.045 16.042 0.000 0.706 0.706 WFsi10 0.093 0.041 2.267 0.023 0.097 0.097 WFsi12 ~
WFhyp10 0.183 0.053 3.455 0.001 0.151 0.151 WFsi10 0.835 0.067 12.522 0.000 0.743 0.743

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.127 0.034 3.703 0.000 0.518 0.518 .WFhyp7 ~~
.WFsi7 0.042 0.011 3.994 0.000 0.259 0.259 .WFhyp10 ~~
.WFsi10 0.037 0.011 3.504 0.000 0.204 0.204 .WFhyp12 ~~
.WFsi12 0.047 0.009 4.989 0.000 0.327 0.327 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.263 0.033 8.091 0.000 0.692 0.692 RIhyp3 0.170 0.030 5.613 0.000 0.444 0.444 RIhyp4 0.211 0.034 6.203 0.000 0.576 0.576 RIhyp5 0.242 0.036 6.660 0.000 0.598 0.598 RIhyp6 0.249 0.039 6.406 0.000 0.628 0.628 RIhyp7 0.244 0.038 6.473 0.000 0.616 0.616 RIhyp8 0.248 0.033 7.616 0.000 0.579 0.579 RIhyp9 0.202 0.025 8.175 0.000 0.422 0.422 RIsi1 0.041 0.036 1.130 0.259 0.135 0.135 RIsi2 0.127 0.044 2.927 0.003 0.607 0.607 RIsi3 0.118 0.034 3.508 0.000 0.362 0.362 RIsi4 -0.061 0.030 -2.050 0.040 -0.148 -0.148 RIsi5 0.188 0.043 4.402 0.000 0.601 0.601 RIsi6 0.055 0.039 1.396 0.163 0.174 0.174 RIhyp2 ~~
RIhyp3 0.215 0.032 6.789 0.000 0.606 0.606 RIhyp4 0.253 0.036 7.034 0.000 0.743 0.743 RIhyp5 0.156 0.038 4.062 0.000 0.414 0.414 RIhyp6 0.172 0.041 4.181 0.000 0.467 0.467 RIhyp7 0.158 0.040 3.980 0.000 0.430 0.430 RIhyp8 0.195 0.034 5.752 0.000 0.492 0.492 RIhyp9 0.279 0.025 11.238 0.000 0.627 0.627 RIsi1 0.028 0.038 0.739 0.460 0.100 0.100 RIsi2 0.056 0.047 1.193 0.233 0.287 0.287 RIsi3 0.094 0.035 2.682 0.007 0.312 0.312 RIsi4 -0.069 0.032 -2.180 0.029 -0.181 -0.181 RIsi5 0.136 0.046 2.964 0.003 0.469 0.469 RIsi6 -0.003 0.042 -0.083 0.934 -0.012 -0.012 RIhyp3 ~~
RIhyp4 0.277 0.033 8.329 0.000 0.807 0.807 RIhyp5 0.089 0.036 2.484 0.013 0.235 0.235 RIhyp6 0.108 0.038 2.814 0.005 0.291 0.291 RIhyp7 0.087 0.037 2.345 0.019 0.235 0.235 RIhyp8 0.219 0.032 6.925 0.000 0.549 0.549 RIhyp9 0.290 0.023 12.658 0.000 0.648 0.648 RIsi1 0.011 0.035 0.307 0.759 0.038 0.038 RIsi2 0.005 0.044 0.123 0.902 0.028 0.028 RIsi3 0.060 0.033 1.849 0.064 0.197 0.197 RIsi4 -0.073 0.030 -2.423 0.015 -0.191 -0.191 RIsi5 0.058 0.044 1.318 0.188 0.198 0.198 RIsi6 -0.085 0.040 -2.153 0.031 -0.288 -0.288 RIhyp4 ~~
RIhyp5 0.131 0.040 3.257 0.001 0.362 0.362 RIhyp6 0.171 0.043 3.943 0.000 0.482 0.482 RIhyp7 0.183 0.042 4.343 0.000 0.516 0.516 RIhyp8 0.218 0.036 6.113 0.000 0.570 0.570 RIhyp9 0.225 0.026 8.787 0.000 0.526 0.526 RIsi1 0.021 0.040 0.520 0.603 0.077 0.077 RIsi2 0.085 0.049 1.724 0.085 0.453 0.453 RIsi3 0.098 0.037 2.662 0.008 0.335 0.335 RIsi4 -0.074 0.033 -2.244 0.025 -0.202 -0.202 RIsi5 0.126 0.049 2.574 0.010 0.449 0.449 RIsi6 -0.011 0.044 -0.261 0.794 -0.040 -0.040 RIhyp5 ~~
RIhyp6 0.374 0.047 7.987 0.000 0.953 0.953 RIhyp7 0.279 0.045 6.215 0.000 0.712 0.712 RIhyp8 0.256 0.038 6.725 0.000 0.607 0.607 RIhyp9 0.158 0.029 5.545 0.000 0.334 0.334 RIsi1 0.006 0.044 0.140 0.889 0.021 0.021 RIsi2 0.040 0.054 0.736 0.462 0.193 0.193 RIsi3 0.025 0.040 0.626 0.531 0.078 0.078 RIsi4 -0.045 0.036 -1.259 0.208 -0.112 -0.112 RIsi5 0.076 0.053 1.423 0.155 0.246 0.246 RIsi6 0.029 0.048 0.591 0.554 0.091 0.091 RIhyp6 ~~
RIhyp7 0.355 0.049 7.305 0.000 0.926 0.926 RIhyp8 0.323 0.041 7.929 0.000 0.781 0.781 RIhyp9 0.182 0.030 6.023 0.000 0.393 0.393 RIsi1 -0.012 0.047 -0.254 0.800 -0.041 -0.041 RIsi2 0.052 0.059 0.877 0.381 0.253 0.253 RIsi3 0.015 0.043 0.344 0.731 0.047 0.047 RIsi4 -0.048 0.039 -1.234 0.217 -0.120 -0.120 RIsi5 0.087 0.058 1.509 0.131 0.289 0.289 RIsi6 0.027 0.052 0.510 0.610 0.087 0.087 RIhyp7 ~~
RIhyp8 0.302 0.040 7.605 0.000 0.730 0.730 RIhyp9 0.153 0.030 5.126 0.000 0.330 0.330 RIsi1 0.027 0.047 0.573 0.567 0.091 0.091 RIsi2 0.045 0.057 0.793 0.428 0.222 0.222 RIsi3 0.039 0.042 0.928 0.354 0.124 0.124 RIsi4 -0.037 0.038 -0.980 0.327 -0.094 -0.094 RIsi5 0.085 0.056 1.527 0.127 0.282 0.282 RIsi6 0.047 0.051 0.924 0.355 0.153 0.153 RIhyp8 ~~
RIhyp9 0.261 0.026 10.229 0.000 0.523 0.523 RIsi1 -0.027 0.039 -0.694 0.488 -0.087 -0.087 RIsi2 0.021 0.047 0.446 0.655 0.097 0.097 RIsi3 0.039 0.036 1.080 0.280 0.114 0.114 RIsi4 -0.081 0.033 -2.482 0.013 -0.189 -0.189 RIsi5 0.059 0.047 1.237 0.216 0.179 0.179 RIsi6 -0.041 0.043 -0.938 0.348 -0.123 -0.123 RIhyp9 ~~
RIsi1 0.044 0.029 1.517 0.129 0.125 0.125 RIsi2 0.013 0.034 0.384 0.701 0.053 0.053 RIsi3 0.086 0.026 3.328 0.001 0.226 0.226 RIsi4 -0.088 0.025 -3.553 0.000 -0.183 -0.183 RIsi5 0.066 0.036 1.821 0.069 0.180 0.180 RIsi6 -0.118 0.031 -3.738 0.000 -0.319 -0.319 RIsi1 ~~
RIsi2 -0.003 0.073 -0.037 0.971 -0.017 -0.017 RIsi3 0.133 0.056 2.382 0.017 0.551 0.551 RIsi4 0.040 0.049 0.816 0.414 0.133 0.133 RIsi5 0.028 0.072 0.382 0.702 0.120 0.120 RIsi6 0.063 0.066 0.953 0.341 0.268 0.268 RIsi2 ~~
RIsi3 -0.039 0.067 -0.581 0.561 -0.232 -0.232 RIsi4 0.062 0.059 1.053 0.292 0.296 0.296 RIsi5 0.153 0.089 1.718 0.086 0.954 0.954 RIsi6 0.047 0.080 0.583 0.560 0.288 0.288 RIsi3 ~~
RIsi4 -0.079 0.045 -1.744 0.081 -0.240 -0.240 RIsi5 -0.013 0.066 -0.196 0.844 -0.052 -0.052 RIsi6 -0.036 0.060 -0.594 0.553 -0.142 -0.142 RIsi4 ~~
RIsi5 0.048 0.058 0.839 0.401 0.154 0.154 RIsi6 0.252 0.055 4.571 0.000 0.793 0.793 RIsi5 ~~
RIsi6 0.067 0.078 0.860 0.390 0.279 0.279

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 0.000 0.000 0.000 .pe84m7 0.000 0.000 0.000 .pe84m10 0.000 0.000 0.000 .pe84m12 0.000 0.000 0.000 .pe85m5 0.000 0.000 0.000 .pe85m7 0.000 0.000 0.000 .pe85m10 0.000 0.000 0.000 .pe85m12 0.000 0.000 0.000 .pe96m5 0.000 0.000 0.000 .pe96m7 0.000 0.000 0.000 .pe96m10 0.000 0.000 0.000 .pe96m12 0.000 0.000 0.000 .pe97m5 0.000 0.000 0.000 .pe97m7 0.000 0.000 0.000 .pe97m10 0.000 0.000 0.000 .pe97m12 0.000 0.000 0.000 .pe92m5 0.000 0.000 0.000 .pe92m7 0.000 0.000 0.000 .pe92m10 0.000 0.000 0.000 .pe92m12 0.000 0.000 0.000 .pe93m5 0.000 0.000 0.000 .pe93m7 0.000 0.000 0.000 .pe93m10 0.000 0.000 0.000 .pe93m12 0.000 0.000 0.000 .pe94m5 0.000 0.000 0.000 .pe94m7 0.000 0.000 0.000 .pe94m10 0.000 0.000 0.000 .pe94m12 0.000 0.000 0.000 .pe95m5 0.000 0.000 0.000 .pe95m7 0.000 0.000 0.000 .pe95m10 0.000 0.000 0.000 .pe95m12 0.000 0.000 0.000 .pe64m5 0.000 0.000 0.000 .pe64m7 0.000 0.000 0.000 .pe64m10 0.000 0.000 0.000 .pe64m12 0.000 0.000 0.000 .pe2m5 0.000 0.000 0.000 .pe2m7 0.000 0.000 0.000 .pe2m10 0.000 0.000 0.000 .pe2m12 0.000 0.000 0.000 .pe4m5 0.000 0.000 0.000 .pe4m7 0.000 0.000 0.000 .pe4m10 0.000 0.000 0.000 .pe4m12 0.000 0.000 0.000 .pe7m5 0.000 0.000 0.000 .pe7m7 0.000 0.000 0.000 .pe7m10 0.000 0.000 0.000 .pe7m12 0.000 0.000 0.000 .pe11m5 0.000 0.000 0.000 .pe11m7 0.000 0.000 0.000 .pe11m10 0.000 0.000 0.000 .pe11m12 0.000 0.000 0.000 .pe13m5 0.000 0.000 0.000 .pe13m7 0.000 0.000 0.000 .pe13m10 0.000 0.000 0.000 .pe13m12 0.000 0.000 0.000 .pe25m5 0.000 0.000 0.000 .pe25m7 0.000 0.000 0.000 .pe25m10 0.000 0.000 0.000 .pe25m12 0.000 0.000 0.000 RIhyp1 0.000 0.000 0.000 RIhyp2 0.000 0.000 0.000 RIhyp3 0.000 0.000 0.000 RIhyp4 0.000 0.000 0.000 RIhyp5 0.000 0.000 0.000 RIhyp6 0.000 0.000 0.000 RIhyp7 0.000 0.000 0.000 RIhyp8 0.000 0.000 0.000 RIhyp9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 .WFhyp7 0.000 0.000 0.000 .WFhyp10 0.000 0.000 0.000 .WFhyp12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe84m5|t1 -0.002 0.027 -0.064 0.949 -0.002 -0.002 pe84m5|t2 0.973 0.032 30.690 0.000 0.973 0.973 pe84m7|t1 0.077 0.027 2.873 0.004 0.077 0.077 pe84m7|t2 1.232 0.036 34.421 0.000 1.232 1.232 pe84m10|t1 0.112 0.027 4.130 0.000 0.112 0.112 pe84m10|t2 1.257 0.037 34.403 0.000 1.257 1.257 pe84m12|t1 0.091 0.027 3.369 0.001 0.091 0.091 pe84m12|t2 1.296 0.037 34.821 0.000 1.296 1.296 pe85m5|t1 -0.932 0.031 -29.869 0.000 -0.932 -0.932 pe85m5|t2 0.266 0.027 9.897 0.000 0.266 0.266 pe85m7|t1 -0.865 0.031 -28.041 0.000 -0.865 -0.865 pe85m7|t2 0.586 0.029 20.490 0.000 0.586 0.586 pe85m10|t1 -0.480 0.028 -16.965 0.000 -0.480 -0.480 pe85m10|t2 0.865 0.031 27.768 0.000 0.865 0.865 pe85m12|t1 -0.290 0.028 -10.527 0.000 -0.290 -0.290 pe85m12|t2 0.951 0.032 29.673 0.000 0.951 0.951 pe96m5|t1 -0.188 0.027 -7.015 0.000 -0.188 -0.188 pe96m5|t2 0.849 0.030 27.946 0.000 0.849 0.849 pe96m7|t1 -0.169 0.027 -6.258 0.000 -0.169 -0.169 pe96m7|t2 0.923 0.031 29.308 0.000 0.923 0.923 pe96m10|t1 -0.036 0.027 -1.341 0.180 -0.036 -0.036 pe96m10|t2 1.072 0.034 31.883 0.000 1.072 1.072 pe96m12|t1 0.056 0.027 2.076 0.038 0.056 0.056 pe96m12|t2 1.117 0.034 32.620 0.000 1.117 1.117 pe97m5|t1 -0.358 0.027 -13.164 0.000 -0.358 -0.358 pe97m5|t2 0.606 0.028 21.331 0.000 0.606 0.606 pe97m7|t1 -0.268 0.027 -9.848 0.000 -0.268 -0.268 pe97m7|t2 0.792 0.030 26.251 0.000 0.792 0.792 pe97m10|t1 0.139 0.027 5.104 0.000 0.139 0.139 pe97m10|t2 1.110 0.034 32.496 0.000 1.110 1.110 pe97m12|t1 0.256 0.027 9.331 0.000 0.256 0.256 pe97m12|t2 1.169 0.035 33.361 0.000 1.169 1.169 pe92m5|t1 0.005 0.027 0.169 0.865 0.005 0.005 pe92m5|t2 0.809 0.030 27.001 0.000 0.809 0.809 pe92m7|t1 0.059 0.027 2.186 0.029 0.059 0.059 pe92m7|t2 0.959 0.032 30.087 0.000 0.959 0.959 pe92m10|t1 0.366 0.028 13.184 0.000 0.366 0.366 pe92m10|t2 1.153 0.035 33.129 0.000 1.153 1.153 pe92m12|t1 0.405 0.028 14.500 0.000 0.405 0.405 pe92m12|t2 1.248 0.036 34.338 0.000 1.248 1.248 pe93m5|t1 0.198 0.027 7.405 0.000 0.198 0.198 pe93m5|t2 0.874 0.031 28.582 0.000 0.874 0.874 pe93m7|t1 0.356 0.027 12.936 0.000 0.356 0.356 pe93m7|t2 1.003 0.032 30.945 0.000 1.003 1.003 pe93m10|t1 0.517 0.028 18.156 0.000 0.517 0.517 pe93m10|t2 1.240 0.036 34.221 0.000 1.240 1.240 pe93m12|t1 0.597 0.029 20.655 0.000 0.597 0.597 pe93m12|t2 1.298 0.037 34.836 0.000 1.298 1.298 pe94m5|t1 0.583 0.028 20.647 0.000 0.583 0.583 pe94m5|t2 1.230 0.035 34.837 0.000 1.230 1.230 pe94m7|t1 0.718 0.030 24.298 0.000 0.718 0.718 pe94m7|t2 1.375 0.038 35.717 0.000 1.375 1.375 pe94m10|t1 0.815 0.031 26.578 0.000 0.815 0.815 pe94m10|t2 1.556 0.043 36.053 0.000 1.556 1.556 pe94m12|t1 0.889 0.031 28.343 0.000 0.889 0.889 pe94m12|t2 1.589 0.044 36.072 0.000 1.589 1.589 pe95m5|t1 -0.006 0.027 -0.212 0.832 -0.006 -0.006 pe95m5|t2 0.509 0.028 18.286 0.000 0.509 0.509 pe95m7|t1 0.149 0.027 5.528 0.000 0.149 0.149 pe95m7|t2 0.767 0.030 25.609 0.000 0.767 0.767 pe95m10|t1 0.288 0.028 10.451 0.000 0.288 0.288 pe95m10|t2 0.940 0.032 29.430 0.000 0.940 0.940 pe95m12|t1 0.465 0.028 16.497 0.000 0.465 0.465 pe95m12|t2 1.079 0.034 32.012 0.000 1.079 1.079 pe64m5|t1 -0.246 0.027 -9.160 0.000 -0.246 -0.246 pe64m5|t2 0.491 0.028 17.686 0.000 0.491 0.491 pe64m7|t1 -0.283 0.027 -10.365 0.000 -0.283 -0.283 pe64m7|t2 0.635 0.029 21.939 0.000 0.635 0.635 pe64m10|t1 -0.075 0.027 -2.746 0.006 -0.075 -0.075 pe64m10|t2 0.869 0.031 27.872 0.000 0.869 0.869 pe64m12|t1 -0.046 0.027 -1.708 0.088 -0.046 -0.046 pe64m12|t2 0.872 0.031 27.939 0.000 0.872 0.872 pe2m5|t1 1.365 0.038 36.090 0.000 1.365 1.365 pe2m5|t2 2.367 0.082 28.719 0.000 2.367 2.367 pe2m7|t1 1.176 0.035 33.748 0.000 1.176 1.176 pe2m7|t2 2.259 0.075 30.173 0.000 2.259 2.259 pe2m10|t1 1.006 0.033 30.734 0.000 1.006 1.006 pe2m10|t2 2.135 0.067 31.753 0.000 2.135 2.135 pe2m12|t1 1.129 0.034 32.821 0.000 1.129 1.129 pe2m12|t2 2.238 0.074 30.252 0.000 2.238 2.238 pe4m5|t1 1.007 0.032 31.404 0.000 1.007 1.007 pe4m5|t2 2.130 0.066 32.506 0.000 2.130 2.130 pe4m7|t1 1.012 0.033 31.125 0.000 1.012 1.012 pe4m7|t2 2.259 0.075 30.173 0.000 2.259 2.259 pe4m10|t1 0.917 0.032 28.934 0.000 0.917 0.917 pe4m10|t2 2.147 0.068 31.589 0.000 2.147 2.147 pe4m12|t1 0.902 0.031 28.642 0.000 0.902 0.902 pe4m12|t2 2.224 0.073 30.475 0.000 2.224 2.224 pe7m5|t1 0.833 0.030 27.602 0.000 0.833 0.833 pe7m5|t2 1.958 0.056 34.655 0.000 1.958 1.958 pe7m7|t1 0.662 0.029 22.714 0.000 0.662 0.662 pe7m7|t2 1.831 0.052 35.391 0.000 1.831 1.831 pe7m10|t1 0.650 0.029 22.180 0.000 0.650 0.650 pe7m10|t2 1.940 0.057 34.118 0.000 1.940 1.940 pe7m12|t1 0.717 0.030 24.065 0.000 0.717 0.717 pe7m12|t2 2.006 0.060 33.431 0.000 2.006 2.006 pe11m5|t1 0.689 0.029 23.796 0.000 0.689 0.689 pe11m5|t2 1.784 0.049 36.154 0.000 1.784 1.784 pe11m7|t1 0.859 0.031 27.885 0.000 0.859 0.859 pe11m7|t2 1.890 0.054 34.916 0.000 1.890 1.890 pe11m10|t1 0.795 0.030 26.081 0.000 0.795 0.795 pe11m10|t2 1.980 0.059 33.697 0.000 1.980 1.980 pe11m12|t1 0.864 0.031 27.757 0.000 0.864 0.864 pe11m12|t2 1.997 0.060 33.525 0.000 1.997 1.997 pe13m5|t1 1.576 0.043 36.801 0.000 1.576 1.576 pe13m5|t2 2.649 0.112 23.551 0.000 2.649 2.649 pe13m7|t1 1.457 0.040 36.170 0.000 1.457 1.457 pe13m7|t2 2.605 0.108 24.096 0.000 2.605 2.605 pe13m10|t1 1.252 0.036 34.360 0.000 1.252 1.252 pe13m10|t2 2.390 0.086 27.724 0.000 2.390 2.390 pe13m12|t1 1.198 0.036 33.740 0.000 1.198 1.198 pe13m12|t2 2.283 0.077 29.527 0.000 2.283 2.283 pe25m5|t1 1.172 0.034 34.106 0.000 1.172 1.172 pe25m5|t2 2.283 0.076 30.143 0.000 2.283 2.283 pe25m7|t1 1.384 0.039 35.794 0.000 1.384 1.384 pe25m7|t2 2.322 0.080 29.135 0.000 2.322 2.322 pe25m10|t1 1.368 0.039 35.369 0.000 1.368 1.368 pe25m10|t2 2.333 0.081 28.695 0.000 2.333 2.333 pe25m12|t1 1.256 0.036 34.420 0.000 1.256 1.256 pe25m12|t2 2.457 0.092 26.575 0.000 2.457 2.457

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 0.393 0.393 0.393 .pe84m7 0.345 0.345 0.345 .pe84m10 0.275 0.275 0.275 .pe84m12 0.257 0.257 0.257 .pe85m5 0.422 0.422 0.422 .pe85m7 0.382 0.382 0.382 .pe85m10 0.270 0.270 0.270 .pe85m12 0.254 0.254 0.254 .pe96m5 0.572 0.572 0.572 .pe96m7 0.450 0.450 0.450 .pe96m10 0.296 0.296 0.296 .pe96m12 0.181 0.181 0.181 .pe97m5 0.396 0.396 0.396 .pe97m7 0.373 0.373 0.373 .pe97m10 0.264 0.264 0.264 .pe97m12 0.207 0.207 0.207 .pe92m5 0.248 0.248 0.248 .pe92m7 0.215 0.215 0.215 .pe92m10 0.182 0.182 0.182 .pe92m12 0.175 0.175 0.175 .pe93m5 0.145 0.145 0.145 .pe93m7 0.150 0.150 0.150 .pe93m10 0.163 0.163 0.163 .pe93m12 0.113 0.113 0.113 .pe94m5 0.198 0.198 0.198 .pe94m7 0.179 0.179 0.179 .pe94m10 0.206 0.206 0.206 .pe94m12 0.153 0.153 0.153 .pe95m5 0.253 0.253 0.253 .pe95m7 0.225 0.225 0.225 .pe95m10 0.290 0.290 0.290 .pe95m12 0.251 0.251 0.251 .pe64m5 0.349 0.349 0.349 .pe64m7 0.380 0.380 0.380 .pe64m10 0.293 0.293 0.293 .pe64m12 0.261 0.261 0.261 .pe2m5 0.474 0.474 0.474 .pe2m7 0.436 0.436 0.436 .pe2m10 0.408 0.408 0.408 .pe2m12 0.310 0.310 0.310 .pe4m5 0.253 0.253 0.253 .pe4m7 0.165 0.165 0.165 .pe4m10 0.213 0.213 0.213 .pe4m12 0.176 0.176 0.176 .pe7m5 0.562 0.562 0.562 .pe7m7 0.429 0.429 0.429 .pe7m10 0.373 0.373 0.373 .pe7m12 0.265 0.265 0.265 .pe11m5 0.328 0.328 0.328 .pe11m7 0.331 0.331 0.331 .pe11m10 0.394 0.394 0.394 .pe11m12 0.291 0.291 0.291 .pe13m5 0.219 0.219 0.219 .pe13m7 0.111 0.111 0.111 .pe13m10 0.113 0.113 0.113 .pe13m12 0.132 0.132 0.132 .pe25m5 0.265 0.265 0.265 .pe25m7 0.231 0.231 0.231 .pe25m10 0.237 0.237 0.237 .pe25m12 0.225 0.225 0.225 RIhyp1 0.410 0.032 12.764 0.000 1.000 1.000 RIhyp2 0.353 0.036 9.874 0.000 1.000 1.000 RIhyp3 0.358 0.031 11.652 0.000 1.000 1.000 RIhyp4 0.328 0.039 8.346 0.000 1.000 1.000 RIhyp5 0.400 0.044 9.102 0.000 1.000 1.000 RIhyp6 0.384 0.051 7.512 0.000 1.000 1.000 RIhyp7 0.384 0.048 7.963 0.000 1.000 1.000 RIhyp8 0.446 0.035 12.744 0.000 1.000 1.000 RIhyp9 0.559 0.019 29.480 0.000 1.000 1.000 RIsi1 0.223 0.065 3.422 0.001 1.000 1.000 RIsi2 0.108 0.095 1.135 0.256 1.000 1.000 RIsi3 0.261 0.054 4.813 0.000 1.000 1.000 RIsi4 0.412 0.045 9.197 0.000 1.000 1.000 RIsi5 0.239 0.090 2.665 0.008 1.000 1.000 RIsi6 0.244 0.079 3.107 0.002 1.000 1.000 WFhyp5 0.197 0.036 5.553 0.000 1.000 1.000 .WFhyp7 0.152 0.017 8.979 0.000 0.619 0.619 .WFhyp10 0.185 0.016 11.378 0.000 0.586 0.586 .WFhyp12 0.141 0.012 12.009 0.000 0.422 0.422 WFsi5 0.303 0.083 3.636 0.000 1.000 1.000 .WFsi7 0.173 0.031 5.500 0.000 0.506 0.506 .WFsi10 0.180 0.028 6.391 0.000 0.487 0.487 .WFsi12 0.145 0.021 6.974 0.000 0.310 0.310

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe84m5 1.000 1.000 1.000 pe84m7 1.000 1.000 1.000 pe84m10 1.000 1.000 1.000 pe84m12 1.000 1.000 1.000 pe85m5 1.000 1.000 1.000 pe85m7 1.000 1.000 1.000 pe85m10 1.000 1.000 1.000 pe85m12 1.000 1.000 1.000 pe96m5 1.000 1.000 1.000 pe96m7 1.000 1.000 1.000 pe96m10 1.000 1.000 1.000 pe96m12 1.000 1.000 1.000 pe97m5 1.000 1.000 1.000 pe97m7 1.000 1.000 1.000 pe97m10 1.000 1.000 1.000 pe97m12 1.000 1.000 1.000 pe92m5 1.000 1.000 1.000 pe92m7 1.000 1.000 1.000 pe92m10 1.000 1.000 1.000 pe92m12 1.000 1.000 1.000 pe93m5 1.000 1.000 1.000 pe93m7 1.000 1.000 1.000 pe93m10 1.000 1.000 1.000 pe93m12 1.000 1.000 1.000 pe94m5 1.000 1.000 1.000 pe94m7 1.000 1.000 1.000 pe94m10 1.000 1.000 1.000 pe94m12 1.000 1.000 1.000 pe95m5 1.000 1.000 1.000 pe95m7 1.000 1.000 1.000 pe95m10 1.000 1.000 1.000 pe95m12 1.000 1.000 1.000 pe64m5 1.000 1.000 1.000 pe64m7 1.000 1.000 1.000 pe64m10 1.000 1.000 1.000 pe64m12 1.000 1.000 1.000 pe2m5 1.000 1.000 1.000 pe2m7 1.000 1.000 1.000 pe2m10 1.000 1.000 1.000 pe2m12 1.000 1.000 1.000 pe4m5 1.000 1.000 1.000 pe4m7 1.000 1.000 1.000 pe4m10 1.000 1.000 1.000 pe4m12 1.000 1.000 1.000 pe7m5 1.000 1.000 1.000 pe7m7 1.000 1.000 1.000 pe7m10 1.000 1.000 1.000 pe7m12 1.000 1.000 1.000 pe11m5 1.000 1.000 1.000 pe11m7 1.000 1.000 1.000 pe11m10 1.000 1.000 1.000 pe11m12 1.000 1.000 1.000 pe13m5 1.000 1.000 1.000 pe13m7 1.000 1.000 1.000 pe13m10 1.000 1.000 1.000 pe13m12 1.000 1.000 1.000 pe25m5 1.000 1.000 1.000 pe25m7 1.000 1.000 1.000 pe25m10 1.000 1.000 1.000 pe25m12 1.000 1.000 1.000

S1 Model fit: Comparative Fit Index (CFI) 0.989 (>0.95) Tucker-Lewis Index (TLI) 0.988 (>0.95)
RMSEA 0.017 (≤ 0.06)
90 Percent confidence interval - lower 0.016 90 Percent confidence interval - upper 0.018
SRMR 0.033 (≤ 0.08)

We can conclude that the model shows very good fit.

RICLPM_multi_hyp_S2: Hyperactivity step 2

Multiple response items RICLPM mother report hyperactivity ADHD symptoms and social isolation: weak invariance (S2).

In our S2 model, we constrain the factor loadings to be invariant over time using the labels a*, b*, c*, d* etc, in the “within” part of the model.

RICLPM_multi_hyp_S2 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyptention (mother report)
  RIhyp1 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIhyp2 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIhyp3 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIhyp4 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIhyp5 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIhyp6 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIhyp7 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIhyp8 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIhyp9 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyptention symptoms at 4 waves (constrained)
  WFhyp5 =~ a*pe84m5 + b*pe85m5 + c*pe96m5 + d*pe97m5 + e*pe92m5 + f*pe93m5 + g*pe94m5 + h*pe95m5 + i*pe64m5
  WFhyp7 =~ a*pe84m7 + b*pe85m7 + c*pe96m7 + d*pe97m7 + e*pe92m7 + f*pe93m7 + g*pe94m7 + h*pe95m7 + i*pe64m7
  WFhyp10 =~ a*pe84m10 + b*pe85m10 + c*pe96m10 + d*pe97m10 + e*pe92m10 + f*pe93m10 + g*pe94m10 + h*pe95m10 + i*pe64m10
  WFhyp12 =~ a*pe84m12 + b*pe85m12 + c*pe96m12 + d*pe97m12 + e*pe92m12 + f*pe93m12 + g*pe94m12 + h*pe95m12 + i*pe64m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  WFsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  WFsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  WFsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPM_multi_hyp_S2.fit <- cfa(RICLPM_multi_hyp_S2, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPM_multi_hyp_S2.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 150 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316 Number of equality constraints 39

Number of observations 2232 Number of missing patterns 43

Model Test User Model: Standard Robust Test Statistic 3015.944 2889.548 Degrees of freedom 1613 1613 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.511 Shift parameter 893.165 simple second-order correction

Model Test Baseline Model:

Test statistic 361279.045 95505.953 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 3.835

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.996 0.986 Tucker-Lewis Index (TLI) 0.996 0.985

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.020 0.019 90 Percent confidence interval - lower 0.019 0.018 90 Percent confidence interval - upper 0.021 0.020 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.039 0.039

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIhyp1 =~
pe84m5 1.000 0.696 0.696 pe84m7 1.000 0.696 0.696 pe84m10 1.000 0.696 0.696 pe84m12 1.000 0.696 0.696 RIhyp2 =~
pe85m5 1.000 0.656 0.656 pe85m7 1.000 0.656 0.656 pe85m10 1.000 0.656 0.656 pe85m12 1.000 0.656 0.656 RIhyp3 =~
pe96m5 1.000 0.615 0.615 pe96m7 1.000 0.615 0.615 pe96m10 1.000 0.615 0.615 pe96m12 1.000 0.615 0.615 RIhyp4 =~
pe97m5 1.000 0.646 0.646 pe97m7 1.000 0.646 0.646 pe97m10 1.000 0.646 0.646 pe97m12 1.000 0.646 0.646 RIhyp5 =~
pe92m5 1.000 0.726 0.726 pe92m7 1.000 0.726 0.726 pe92m10 1.000 0.726 0.726 pe92m12 1.000 0.726 0.726 RIhyp6 =~
pe93m5 1.000 0.739 0.739 pe93m7 1.000 0.739 0.739 pe93m10 1.000 0.739 0.739 pe93m12 1.000 0.739 0.739 RIhyp7 =~
pe94m5 1.000 0.730 0.730 pe94m7 1.000 0.730 0.730 pe94m10 1.000 0.730 0.730 pe94m12 1.000 0.730 0.730 RIhyp8 =~
pe95m5 1.000 0.742 0.742 pe95m7 1.000 0.742 0.742 pe95m10 1.000 0.742 0.742 pe95m12 1.000 0.742 0.742 RIhyp9 =~
pe64m5 1.000 0.763 0.763 pe64m7 1.000 0.763 0.763 pe64m10 1.000 0.763 0.763 pe64m12 1.000 0.763 0.763 RIsi1 =~
pe2m5 1.000 0.541 0.541 pe2m7 1.000 0.541 0.541 pe2m10 1.000 0.541 0.541 pe2m12 1.000 0.541 0.541 RIsi2 =~
pe4m5 1.000 0.568 0.568 pe4m7 1.000 0.568 0.568 pe4m10 1.000 0.568 0.568 pe4m12 1.000 0.568 0.568 RIsi3 =~
pe7m5 1.000 0.528 0.528 pe7m7 1.000 0.528 0.528 pe7m10 1.000 0.528 0.528 pe7m12 1.000 0.528 0.528 RIsi4 =~
pe11m5 1.000 0.702 0.702 pe11m7 1.000 0.702 0.702 pe11m10 1.000 0.702 0.702 pe11m12 1.000 0.702 0.702 RIsi5 =~
pe13m5 1.000 0.655 0.655 pe13m7 1.000 0.655 0.655 pe13m10 1.000 0.655 0.655 pe13m12 1.000 0.655 0.655 RIsi6 =~
pe25m5 1.000 0.641 0.641 pe25m7 1.000 0.641 0.641 pe25m10 1.000 0.641 0.641 pe25m12 1.000 0.641 0.641 WFhyp5 =~
pe84m5 (a) 1.000 0.395 0.395 pe85m5 (b) 1.122 0.047 23.927 0.000 0.443 0.443 pe96m5 (c) 1.261 0.060 20.998 0.000 0.498 0.498 pe97m5 (d) 1.201 0.053 22.776 0.000 0.474 0.474 pe92m5 (e) 1.091 0.048 22.607 0.000 0.431 0.431 pe93m5 (f) 1.129 0.049 22.999 0.000 0.445 0.445 pe94m5 (g) 1.083 0.053 20.306 0.000 0.427 0.427 pe95m5 (h) 0.873 0.047 18.540 0.000 0.345 0.345 pe64m5 (i) 0.729 0.049 14.964 0.000 0.288 0.288 WFhyp7 =~
pe84m7 (a) 1.000 0.432 0.432 pe85m7 (b) 1.122 0.047 23.927 0.000 0.485 0.485 pe96m7 (c) 1.261 0.060 20.998 0.000 0.545 0.545 pe97m7 (d) 1.201 0.053 22.776 0.000 0.519 0.519 pe92m7 (e) 1.091 0.048 22.607 0.000 0.472 0.472 pe93m7 (f) 1.129 0.049 22.999 0.000 0.488 0.488 pe94m7 (g) 1.083 0.053 20.306 0.000 0.468 0.468 pe95m7 (h) 0.873 0.047 18.540 0.000 0.377 0.377 pe64m7 (i) 0.729 0.049 14.964 0.000 0.315 0.315 WFhyp10 =~
pe84m10 (a) 1.000 0.480 0.480 pe85m10 (b) 1.122 0.047 23.927 0.000 0.539 0.539 pe96m10 (c) 1.261 0.060 20.998 0.000 0.606 0.606 pe97m10 (d) 1.201 0.053 22.776 0.000 0.577 0.577 pe92m10 (e) 1.091 0.048 22.607 0.000 0.524 0.524 pe93m10 (f) 1.129 0.049 22.999 0.000 0.542 0.542 pe94m10 (g) 1.083 0.053 20.306 0.000 0.520 0.520 pe95m10 (h) 0.873 0.047 18.540 0.000 0.419 0.419 pe64m10 (i) 0.729 0.049 14.964 0.000 0.350 0.350 WFhyp12 =~
pe84m12 (a) 1.000 0.516 0.516 pe85m12 (b) 1.122 0.047 23.927 0.000 0.579 0.579 pe96m12 (c) 1.261 0.060 20.998 0.000 0.651 0.651 pe97m12 (d) 1.201 0.053 22.776 0.000 0.620 0.620 pe92m12 (e) 1.091 0.048 22.607 0.000 0.563 0.563 pe93m12 (f) 1.129 0.049 22.999 0.000 0.582 0.582 pe94m12 (g) 1.083 0.053 20.306 0.000 0.559 0.559 pe95m12 (h) 0.873 0.047 18.540 0.000 0.451 0.451 pe64m12 (i) 0.729 0.049 14.964 0.000 0.376 0.376 WFsi5 =~
pe2m5 (j) 1.000 0.523 0.523 pe4m5 (k) 1.167 0.090 12.926 0.000 0.611 0.611 pe7m5 (l) 1.077 0.087 12.380 0.000 0.563 0.563 pe11m5 (m) 0.680 0.072 9.400 0.000 0.356 0.356 pe13m5 (n) 1.114 0.096 11.666 0.000 0.583 0.583 pe25m5 (o) 0.989 0.088 11.291 0.000 0.517 0.517 WFsi7 =~
pe2m7 (j) 1.000 0.593 0.593 pe4m7 (k) 1.167 0.090 12.926 0.000 0.692 0.692 pe7m7 (l) 1.077 0.087 12.380 0.000 0.639 0.639 pe11m7 (m) 0.680 0.072 9.400 0.000 0.403 0.403 pe13m7 (n) 1.114 0.096 11.666 0.000 0.661 0.661 pe25m7 (o) 0.989 0.088 11.291 0.000 0.586 0.586 WFsi10 =~
pe2m10 (j) 1.000 0.587 0.587 pe4m10 (k) 1.167 0.090 12.926 0.000 0.685 0.685 pe7m10 (l) 1.077 0.087 12.380 0.000 0.632 0.632 pe11m10 (m) 0.680 0.072 9.400 0.000 0.399 0.399 pe13m10 (n) 1.114 0.096 11.666 0.000 0.654 0.654 pe25m10 (o) 0.989 0.088 11.291 0.000 0.580 0.580 WFsi12 =~
pe2m12 (j) 1.000 0.619 0.619 pe4m12 (k) 1.167 0.090 12.926 0.000 0.722 0.722 pe7m12 (l) 1.077 0.087 12.380 0.000 0.666 0.666 pe11m12 (m) 0.680 0.072 9.400 0.000 0.420 0.420 pe13m12 (n) 1.114 0.096 11.666 0.000 0.689 0.689 pe25m12 (o) 0.989 0.088 11.291 0.000 0.612 0.612

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.371 0.070 5.305 0.000 0.339 0.339 WFsi5 -0.035 0.060 -0.579 0.562 -0.042 -0.042 WFsi7 ~
WFhyp5 -0.023 0.100 -0.233 0.815 -0.015 -0.015 WFsi5 0.614 0.077 7.994 0.000 0.541 0.541 WFhyp10 ~
WFhyp7 0.491 0.055 8.881 0.000 0.442 0.442 WFsi7 0.024 0.052 0.455 0.649 0.029 0.029 WFsi10 ~
WFhyp7 0.037 0.074 0.496 0.620 0.027 0.027 WFsi7 0.546 0.071 7.669 0.000 0.552 0.552 WFhyp12 ~
WFhyp10 0.733 0.035 21.099 0.000 0.682 0.682 WFsi10 -0.047 0.038 -1.238 0.216 -0.053 -0.053 WFsi12 ~
WFhyp10 0.089 0.057 1.556 0.120 0.069 0.069 WFsi10 0.756 0.045 16.618 0.000 0.717 0.717

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.018 0.021 0.891 0.373 0.089 0.089 .WFhyp7 ~~
.WFsi7 0.058 0.012 4.765 0.000 0.288 0.288 .WFhyp10 ~~
.WFsi10 0.055 0.012 4.650 0.000 0.263 0.263 .WFhyp12 ~~
.WFsi12 0.067 0.010 6.441 0.000 0.416 0.416 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.333 0.020 16.456 0.000 0.728 0.728 RIhyp3 0.207 0.022 9.533 0.000 0.483 0.483 RIhyp4 0.287 0.021 13.365 0.000 0.638 0.638 RIhyp5 0.341 0.021 16.282 0.000 0.673 0.673 RIhyp6 0.364 0.021 17.001 0.000 0.708 0.708 RIhyp7 0.354 0.021 16.476 0.000 0.696 0.696 RIhyp8 0.342 0.019 17.940 0.000 0.661 0.661 RIhyp9 0.240 0.018 13.003 0.000 0.453 0.453 RIsi1 0.153 0.025 6.045 0.000 0.407 0.407 RIsi2 0.287 0.026 11.062 0.000 0.726 0.726 RIsi3 0.220 0.025 8.925 0.000 0.600 0.600 RIsi4 0.036 0.022 1.691 0.091 0.074 0.074 RIsi5 0.339 0.027 12.377 0.000 0.743 0.743 RIsi6 0.194 0.026 7.409 0.000 0.434 0.434 RIhyp2 ~~
RIhyp3 0.248 0.023 10.855 0.000 0.613 0.613 RIhyp4 0.326 0.022 14.638 0.000 0.770 0.770 RIhyp5 0.255 0.022 11.553 0.000 0.534 0.534 RIhyp6 0.288 0.023 12.729 0.000 0.595 0.595 RIhyp7 0.269 0.023 11.853 0.000 0.562 0.562 RIhyp8 0.291 0.020 14.621 0.000 0.597 0.597 RIhyp9 0.315 0.018 17.313 0.000 0.629 0.629 RIsi1 0.147 0.026 5.637 0.000 0.414 0.414 RIsi2 0.225 0.028 8.003 0.000 0.603 0.603 RIsi3 0.202 0.026 7.728 0.000 0.583 0.583 RIsi4 0.034 0.021 1.573 0.116 0.073 0.073 RIsi5 0.295 0.029 10.348 0.000 0.687 0.687 RIsi6 0.143 0.027 5.360 0.000 0.341 0.341 RIhyp3 ~~
RIhyp4 0.309 0.024 12.935 0.000 0.778 0.778 RIhyp5 0.147 0.024 6.206 0.000 0.330 0.330 RIhyp6 0.181 0.024 7.468 0.000 0.399 0.399 RIhyp7 0.156 0.024 6.493 0.000 0.348 0.348 RIhyp8 0.280 0.021 13.519 0.000 0.615 0.615 RIhyp9 0.303 0.019 16.218 0.000 0.645 0.645 RIsi1 0.107 0.027 3.891 0.000 0.321 0.321 RIsi2 0.144 0.030 4.715 0.000 0.412 0.412 RIsi3 0.146 0.028 5.234 0.000 0.449 0.449 RIsi4 0.010 0.022 0.459 0.646 0.024 0.024 RIsi5 0.189 0.031 6.156 0.000 0.469 0.469 RIsi6 0.036 0.029 1.244 0.213 0.091 0.091 RIhyp4 ~~
RIhyp5 0.237 0.023 10.171 0.000 0.506 0.506 RIhyp6 0.296 0.024 12.331 0.000 0.621 0.621 RIhyp7 0.302 0.024 12.519 0.000 0.641 0.641 RIhyp8 0.320 0.021 15.538 0.000 0.668 0.668 RIhyp9 0.265 0.019 14.230 0.000 0.538 0.538 RIsi1 0.148 0.027 5.496 0.000 0.425 0.425 RIsi2 0.266 0.029 9.136 0.000 0.727 0.727 RIsi3 0.213 0.027 7.879 0.000 0.625 0.625 RIsi4 0.036 0.022 1.622 0.105 0.079 0.079 RIsi5 0.296 0.030 9.957 0.000 0.701 0.701 RIsi6 0.146 0.028 5.234 0.000 0.353 0.353 RIhyp5 ~~
RIhyp6 0.531 0.022 23.676 0.000 0.990 0.990 RIhyp7 0.427 0.023 18.592 0.000 0.805 0.805 RIhyp8 0.381 0.020 18.937 0.000 0.708 0.708 RIhyp9 0.213 0.020 10.820 0.000 0.384 0.384 RIsi1 0.146 0.027 5.427 0.000 0.372 0.372 RIsi2 0.238 0.029 8.241 0.000 0.576 0.576 RIsi3 0.153 0.026 5.776 0.000 0.399 0.399 RIsi4 0.075 0.022 3.327 0.001 0.146 0.146 RIsi5 0.262 0.030 8.855 0.000 0.552 0.552 RIsi6 0.200 0.027 7.292 0.000 0.430 0.430 RIhyp6 ~~
RIhyp7 0.534 0.023 22.979 0.000 0.991 0.991 RIhyp8 0.471 0.020 23.382 0.000 0.859 0.859 RIhyp9 0.248 0.020 12.230 0.000 0.439 0.439 RIsi1 0.144 0.028 5.117 0.000 0.360 0.360 RIsi2 0.271 0.030 9.030 0.000 0.646 0.646 RIsi3 0.158 0.028 5.700 0.000 0.404 0.404 RIsi4 0.086 0.023 3.708 0.000 0.165 0.165 RIsi5 0.294 0.031 9.431 0.000 0.608 0.608 RIsi6 0.217 0.029 7.604 0.000 0.459 0.459 RIhyp7 ~~
RIhyp8 0.442 0.021 21.243 0.000 0.817 0.817 RIhyp9 0.215 0.021 10.279 0.000 0.386 0.386 RIsi1 0.176 0.028 6.225 0.000 0.446 0.446 RIsi2 0.255 0.030 8.606 0.000 0.615 0.615 RIsi3 0.175 0.027 6.411 0.000 0.455 0.455 RIsi4 0.090 0.023 3.852 0.000 0.176 0.176 RIsi5 0.283 0.031 9.226 0.000 0.592 0.592 RIsi6 0.229 0.028 8.055 0.000 0.489 0.489 RIhyp8 ~~
RIhyp9 0.315 0.018 17.497 0.000 0.555 0.555 RIsi1 0.097 0.025 3.928 0.000 0.243 0.243 RIsi2 0.197 0.025 7.729 0.000 0.467 0.467 RIsi3 0.153 0.024 6.457 0.000 0.390 0.390 RIsi4 0.025 0.022 1.176 0.240 0.049 0.049 RIsi5 0.224 0.028 8.058 0.000 0.462 0.462 RIsi6 0.112 0.027 4.212 0.000 0.235 0.235 RIhyp9 ~~
RIsi1 0.114 0.023 5.020 0.000 0.277 0.277 RIsi2 0.114 0.024 4.800 0.000 0.262 0.262 RIsi3 0.150 0.021 7.092 0.000 0.371 0.371 RIsi4 -0.027 0.020 -1.342 0.180 -0.050 -0.050 RIsi5 0.161 0.026 6.132 0.000 0.321 0.321 RIsi6 -0.030 0.025 -1.218 0.223 -0.062 -0.062 RIsi1 ~~
RIsi2 0.120 0.053 2.269 0.023 0.391 0.391 RIsi3 0.159 0.049 3.231 0.001 0.556 0.556 RIsi4 0.119 0.037 3.255 0.001 0.315 0.315 RIsi5 0.142 0.056 2.556 0.011 0.401 0.401 RIsi6 0.174 0.048 3.634 0.000 0.501 0.501 RIsi2 ~~
RIsi3 0.046 0.054 0.869 0.385 0.155 0.155 RIsi4 0.199 0.040 5.018 0.000 0.499 0.499 RIsi5 0.359 0.062 5.816 0.000 0.966 0.966 RIsi6 0.243 0.054 4.498 0.000 0.667 0.667 RIsi3 ~~
RIsi4 -0.023 0.035 -0.641 0.522 -0.061 -0.061 RIsi5 0.066 0.056 1.168 0.243 0.191 0.191 RIsi6 0.043 0.048 0.904 0.366 0.127 0.127 RIsi4 ~~
RIsi5 0.176 0.043 4.112 0.000 0.382 0.382 RIsi6 0.375 0.037 10.069 0.000 0.834 0.834 RIsi5 ~~
RIsi6 0.249 0.057 4.387 0.000 0.592 0.592

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 0.360 0.360 0.360 .pe84m7 0.329 0.329 0.329 .pe84m10 0.285 0.285 0.285 .pe84m12 0.249 0.249 0.249 .pe85m5 0.373 0.373 0.373 .pe85m7 0.334 0.334 0.334 .pe85m10 0.279 0.279 0.279 .pe85m12 0.234 0.234 0.234 .pe96m5 0.374 0.374 0.374 .pe96m7 0.325 0.325 0.325 .pe96m10 0.255 0.255 0.255 .pe96m12 0.199 0.199 0.199 .pe97m5 0.359 0.359 0.359 .pe97m7 0.314 0.314 0.314 .pe97m10 0.250 0.250 0.250 .pe97m12 0.199 0.199 0.199 .pe92m5 0.287 0.287 0.287 .pe92m7 0.250 0.250 0.250 .pe92m10 0.197 0.197 0.197 .pe92m12 0.155 0.155 0.155 .pe93m5 0.256 0.256 0.256 .pe93m7 0.216 0.216 0.216 .pe93m10 0.160 0.160 0.160 .pe93m12 0.115 0.115 0.115 .pe94m5 0.284 0.284 0.284 .pe94m7 0.248 0.248 0.248 .pe94m10 0.197 0.197 0.197 .pe94m12 0.155 0.155 0.155 .pe95m5 0.331 0.331 0.331 .pe95m7 0.307 0.307 0.307 .pe95m10 0.274 0.274 0.274 .pe95m12 0.246 0.246 0.246 .pe64m5 0.334 0.334 0.334 .pe64m7 0.318 0.318 0.318 .pe64m10 0.295 0.295 0.295 .pe64m12 0.276 0.276 0.276 .pe2m5 0.434 0.434 0.434 .pe2m7 0.356 0.356 0.356 .pe2m10 0.363 0.363 0.363 .pe2m12 0.325 0.325 0.325 .pe4m5 0.305 0.305 0.305 .pe4m7 0.199 0.199 0.199 .pe4m10 0.208 0.208 0.208 .pe4m12 0.156 0.156 0.156 .pe7m5 0.404 0.404 0.404 .pe7m7 0.314 0.314 0.314 .pe7m10 0.322 0.322 0.322 .pe7m12 0.277 0.277 0.277 .pe11m5 0.380 0.380 0.380 .pe11m7 0.344 0.344 0.344 .pe11m10 0.348 0.348 0.348 .pe11m12 0.330 0.330 0.330 .pe13m5 0.231 0.231 0.231 .pe13m7 0.135 0.135 0.135 .pe13m10 0.144 0.144 0.144 .pe13m12 0.096 0.096 0.096 .pe25m5 0.321 0.321 0.321 .pe25m7 0.245 0.245 0.245 .pe25m10 0.252 0.252 0.252 .pe25m12 0.215 0.215 0.215 RIhyp1 0.485 0.020 24.117 0.000 1.000 1.000 RIhyp2 0.431 0.022 19.375 0.000 1.000 1.000 RIhyp3 0.378 0.025 14.829 0.000 1.000 1.000 RIhyp4 0.417 0.025 16.819 0.000 1.000 1.000 RIhyp5 0.528 0.022 23.715 0.000 1.000 1.000 RIhyp6 0.546 0.024 22.800 0.000 1.000 1.000 RIhyp7 0.533 0.025 21.637 0.000 1.000 1.000 RIhyp8 0.551 0.018 29.865 0.000 1.000 1.000 RIhyp9 0.583 0.015 38.727 0.000 1.000 1.000 RIsi1 0.292 0.052 5.609 0.000 1.000 1.000 RIsi2 0.322 0.062 5.181 0.000 1.000 1.000 RIsi3 0.279 0.052 5.362 0.000 1.000 1.000 RIsi4 0.493 0.030 16.313 0.000 1.000 1.000 RIsi5 0.429 0.064 6.748 0.000 1.000 1.000 RIsi6 0.411 0.052 7.880 0.000 1.000 1.000 WFhyp5 0.156 0.017 8.922 0.000 1.000 1.000 .WFhyp7 0.165 0.014 11.896 0.000 0.886 0.886 .WFhyp10 0.184 0.015 12.544 0.000 0.799 0.799 .WFhyp12 0.147 0.012 12.161 0.000 0.552 0.552 WFsi5 0.274 0.054 5.052 0.000 1.000 1.000 .WFsi7 0.249 0.033 7.495 0.000 0.708 0.708 .WFsi10 0.237 0.031 7.586 0.000 0.688 0.688 .WFsi12 0.174 0.026 6.791 0.000 0.454 0.454

S2 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.986 (>0.95) Change in CFI: 0.003 (increase) - worse fit Tucker-Lewis Index (TLI) 0.985 (>0.95) Change in TLI: 0.003 (increase) - worse fit RMSEA 0.019 (≤ 0.06) Change in RMSEA: 0.001 (decrease) - worse fit 90 Percent confidence interval - lower 0.018 90 Percent confidence interval - upper 0.020
SRMR 0.040 (≤ 0.08) Change in SRMR: 0.006 (increase) - worse fit (to be expected)

summary(semTools::compareFit(RICLPM_multi_hyp_S1.fit, RICLPM_multi_hyp_S2.fit, nested = TRUE)) #† indicates the best fitting model

Nested Model Comparison

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

                      Df AIC BIC  Chisq Chisq diff Df diff Pr(>Chisq)

RICLPM_multi_hyp_S1.fit 1574 2072.2
RICLPM_multi_hyp_S2.fit 1613 3015.9 214.53 39 < 2.2e-16 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Model Fit Indices

                    chisq.scaled df.scaled pvalue.scaled rmsea.scaled

RICLPM_multi_hyp_S1.fit 2559.537† 1574 .000 .017† RICLPM_multi_hyp_S2.fit 2889.548 1613 .000 .019 cfi.scaled tli.scaled srmr RICLPM_multi_hyp_S1.fit .989† .988† .033† RICLPM_multi_hyp_S2.fit .986 .985 .039

Differences in Fit Indices

                                              df.scaled rmsea.scaled

RICLPM_multi_hyp_S2.fit - RICLPM_multi_hyp_S1.fit 39 0.002 cfi.scaled tli.scaled srmr RICLPM_multi_hyp_S2.fit - RICLPM_multi_hyp_S1.fit -0.003 -0.003 0.006

Significantly worse fit to include the restrictions, p<0.0001.But The difference in model fit is still smaller than 0.01 - so we can assume that weak invariance holds - even though there is a significant chi square test.

RICLPM_multi_hyp_S3

Multiple response items RICLPM mother report hyperactivity ADHD symptoms and social isolation: Step 3

Fitting a model with constraints to ensure strong factorial invariance, with a random intercept for each indicator separately.

RICLPM_multi_hyp_S3 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyptention (mother report)
  RIhyp1 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIhyp2 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIhyp3 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIhyp4 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIhyp5 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIhyp6 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIhyp7 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIhyp8 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIhyp9 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyperactivity symptoms at 4 waves (constrained)
  WFhyp5 =~ a*pe84m5 + b*pe85m5 + c*pe96m5 + d*pe97m5 + e*pe92m5 + f*pe93m5 + g*pe94m5 + h*pe95m5 + i*pe64m5
  WFhyp7 =~ a*pe84m7 + b*pe85m7 + c*pe96m7 + d*pe97m7 + e*pe92m7 + f*pe93m7 + g*pe94m7 + h*pe95m7 + i*pe64m7
  WFhyp10 =~ a*pe84m10 + b*pe85m10 + c*pe96m10 + d*pe97m10 + e*pe92m10 + f*pe93m10 + g*pe94m10 + h*pe95m10 + i*pe64m10
  WFhyp12 =~ a*pe84m12 + b*pe85m12 + c*pe96m12 + d*pe97m12 + e*pe92m12 + f*pe93m12 + g*pe94m12 + h*pe95m12 + i*pe64m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  WFsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  WFsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  WFsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  pe84m5 + pe84m7 + pe84m10 + pe84m12 ~ p*1
  pe85m5 + pe85m7 + pe85m10 + pe85m12 ~ q*1
  pe96m5 + pe96m7 + pe96m10 + pe96m12 ~ r*1
  pe97m5 + pe97m7 + pe97m10 + pe97m12 ~ s*1
  pe92m5 + pe92m7 + pe92m10 + pe92m12 ~ t*1
  pe93m5 + pe93m7 + pe93m10 + pe93m12 ~ u*1
  pe94m5 + pe94m7 + pe94m10 + pe94m12 ~ v*1
  pe95m5 + pe95m7 + pe95m10 + pe95m12 ~ w*1
  pe64m5 + pe64m7 + pe64m10 + pe64m12 ~ x*1
  
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ y*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ z*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ aa*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ ab*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ ac*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ ad*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  WFhyp7 + WFhyp10 + WFhyp12 + WFsi7 + WFsi10 + WFsi12 ~ 1
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPM_multi_hyp_S3.fit <- cfa(RICLPM_multi_hyp_S3, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

RICLPM_multi_hyp_S3.fit.summary <- summary(RICLPM_multi_hyp_S3.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 152 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 382 Number of equality constraints 84

Number of observations 2232 Number of missing patterns 43

Model Test User Model: Standard Robust Test Statistic 3015.944 2860.210 Degrees of freedom 1592 1592 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.521 Shift parameter 876.866 simple second-order correction

Model Test Baseline Model:

Test statistic 361279.045 95505.953 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 3.835

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.996 0.986 Tucker-Lewis Index (TLI) 0.996 0.985

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.020 0.019 90 Percent confidence interval - lower 0.019 0.018 90 Percent confidence interval - upper 0.021 0.020 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.039 0.039

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIhyp1 =~
pe84m5 1.000 0.696 0.696 pe84m7 1.000 0.696 0.696 pe84m10 1.000 0.696 0.696 pe84m12 1.000 0.696 0.696 RIhyp2 =~
pe85m5 1.000 0.656 0.656 pe85m7 1.000 0.656 0.656 pe85m10 1.000 0.656 0.656 pe85m12 1.000 0.656 0.656 RIhyp3 =~
pe96m5 1.000 0.615 0.615 pe96m7 1.000 0.615 0.615 pe96m10 1.000 0.615 0.615 pe96m12 1.000 0.615 0.615 RIhyp4 =~
pe97m5 1.000 0.646 0.646 pe97m7 1.000 0.646 0.646 pe97m10 1.000 0.646 0.646 pe97m12 1.000 0.646 0.646 RIhyp5 =~
pe92m5 1.000 0.726 0.726 pe92m7 1.000 0.726 0.726 pe92m10 1.000 0.726 0.726 pe92m12 1.000 0.726 0.726 RIhyp6 =~
pe93m5 1.000 0.739 0.739 pe93m7 1.000 0.739 0.739 pe93m10 1.000 0.739 0.739 pe93m12 1.000 0.739 0.739 RIhyp7 =~
pe94m5 1.000 0.730 0.730 pe94m7 1.000 0.730 0.730 pe94m10 1.000 0.730 0.730 pe94m12 1.000 0.730 0.730 RIhyp8 =~
pe95m5 1.000 0.742 0.742 pe95m7 1.000 0.742 0.742 pe95m10 1.000 0.742 0.742 pe95m12 1.000 0.742 0.742 RIhyp9 =~
pe64m5 1.000 0.763 0.763 pe64m7 1.000 0.763 0.763 pe64m10 1.000 0.763 0.763 pe64m12 1.000 0.763 0.763 RIsi1 =~
pe2m5 1.000 0.541 0.541 pe2m7 1.000 0.541 0.541 pe2m10 1.000 0.541 0.541 pe2m12 1.000 0.541 0.541 RIsi2 =~
pe4m5 1.000 0.568 0.568 pe4m7 1.000 0.568 0.568 pe4m10 1.000 0.568 0.568 pe4m12 1.000 0.568 0.568 RIsi3 =~
pe7m5 1.000 0.528 0.528 pe7m7 1.000 0.528 0.528 pe7m10 1.000 0.528 0.528 pe7m12 1.000 0.528 0.528 RIsi4 =~
pe11m5 1.000 0.702 0.702 pe11m7 1.000 0.702 0.702 pe11m10 1.000 0.702 0.702 pe11m12 1.000 0.702 0.702 RIsi5 =~
pe13m5 1.000 0.655 0.655 pe13m7 1.000 0.655 0.655 pe13m10 1.000 0.655 0.655 pe13m12 1.000 0.655 0.655 RIsi6 =~
pe25m5 1.000 0.641 0.641 pe25m7 1.000 0.641 0.641 pe25m10 1.000 0.641 0.641 pe25m12 1.000 0.641 0.641 WFhyp5 =~
pe84m5 (a) 1.000 0.395 0.395 pe85m5 (b) 1.122 0.047 23.927 0.000 0.443 0.443 pe96m5 (c) 1.261 0.060 20.998 0.000 0.498 0.498 pe97m5 (d) 1.201 0.053 22.776 0.000 0.474 0.474 pe92m5 (e) 1.091 0.048 22.607 0.000 0.431 0.431 pe93m5 (f) 1.129 0.049 22.999 0.000 0.445 0.445 pe94m5 (g) 1.083 0.053 20.306 0.000 0.427 0.427 pe95m5 (h) 0.873 0.047 18.540 0.000 0.345 0.345 pe64m5 (i) 0.729 0.049 14.964 0.000 0.288 0.288 WFhyp7 =~
pe84m7 (a) 1.000 0.432 0.432 pe85m7 (b) 1.122 0.047 23.927 0.000 0.485 0.485 pe96m7 (c) 1.261 0.060 20.998 0.000 0.545 0.545 pe97m7 (d) 1.201 0.053 22.776 0.000 0.519 0.519 pe92m7 (e) 1.091 0.048 22.607 0.000 0.472 0.472 pe93m7 (f) 1.129 0.049 22.999 0.000 0.488 0.488 pe94m7 (g) 1.083 0.053 20.306 0.000 0.468 0.468 pe95m7 (h) 0.873 0.047 18.540 0.000 0.377 0.377 pe64m7 (i) 0.729 0.049 14.964 0.000 0.315 0.315 WFhyp10 =~
pe84m10 (a) 1.000 0.480 0.480 pe85m10 (b) 1.122 0.047 23.927 0.000 0.539 0.539 pe96m10 (c) 1.261 0.060 20.998 0.000 0.606 0.606 pe97m10 (d) 1.201 0.053 22.776 0.000 0.577 0.577 pe92m10 (e) 1.091 0.048 22.607 0.000 0.524 0.524 pe93m10 (f) 1.129 0.049 22.999 0.000 0.542 0.542 pe94m10 (g) 1.083 0.053 20.306 0.000 0.520 0.520 pe95m10 (h) 0.873 0.047 18.540 0.000 0.419 0.419 pe64m10 (i) 0.729 0.049 14.964 0.000 0.350 0.350 WFhyp12 =~
pe84m12 (a) 1.000 0.516 0.516 pe85m12 (b) 1.122 0.047 23.927 0.000 0.579 0.579 pe96m12 (c) 1.261 0.060 20.998 0.000 0.651 0.651 pe97m12 (d) 1.201 0.053 22.776 0.000 0.620 0.620 pe92m12 (e) 1.091 0.048 22.607 0.000 0.563 0.563 pe93m12 (f) 1.129 0.049 22.999 0.000 0.582 0.582 pe94m12 (g) 1.083 0.053 20.306 0.000 0.559 0.559 pe95m12 (h) 0.873 0.047 18.540 0.000 0.451 0.451 pe64m12 (i) 0.729 0.049 14.964 0.000 0.376 0.376 WFsi5 =~
pe2m5 (j) 1.000 0.523 0.523 pe4m5 (k) 1.167 0.090 12.926 0.000 0.611 0.611 pe7m5 (l) 1.077 0.087 12.380 0.000 0.563 0.563 pe11m5 (m) 0.680 0.072 9.400 0.000 0.356 0.356 pe13m5 (n) 1.114 0.096 11.666 0.000 0.583 0.583 pe25m5 (o) 0.989 0.088 11.291 0.000 0.517 0.517 WFsi7 =~
pe2m7 (j) 1.000 0.593 0.593 pe4m7 (k) 1.167 0.090 12.926 0.000 0.692 0.692 pe7m7 (l) 1.077 0.087 12.380 0.000 0.638 0.638 pe11m7 (m) 0.680 0.072 9.400 0.000 0.403 0.403 pe13m7 (n) 1.114 0.096 11.666 0.000 0.661 0.661 pe25m7 (o) 0.989 0.088 11.291 0.000 0.586 0.586 WFsi10 =~
pe2m10 (j) 1.000 0.587 0.587 pe4m10 (k) 1.167 0.090 12.926 0.000 0.685 0.685 pe7m10 (l) 1.077 0.087 12.380 0.000 0.632 0.632 pe11m10 (m) 0.680 0.072 9.400 0.000 0.399 0.399 pe13m10 (n) 1.114 0.096 11.666 0.000 0.654 0.654 pe25m10 (o) 0.989 0.088 11.291 0.000 0.580 0.580 WFsi12 =~
pe2m12 (j) 1.000 0.619 0.619 pe4m12 (k) 1.167 0.090 12.926 0.000 0.722 0.722 pe7m12 (l) 1.077 0.087 12.380 0.000 0.666 0.666 pe11m12 (m) 0.680 0.072 9.400 0.000 0.420 0.420 pe13m12 (n) 1.114 0.096 11.666 0.000 0.689 0.689 pe25m12 (o) 0.989 0.088 11.291 0.000 0.612 0.612

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.371 0.070 5.305 0.000 0.339 0.339 WFsi5 -0.035 0.060 -0.579 0.562 -0.042 -0.042 WFsi7 ~
WFhyp5 -0.023 0.100 -0.233 0.815 -0.015 -0.015 WFsi5 0.614 0.077 7.994 0.000 0.541 0.541 WFhyp10 ~
WFhyp7 0.491 0.055 8.881 0.000 0.442 0.442 WFsi7 0.024 0.052 0.455 0.649 0.029 0.029 WFsi10 ~
WFhyp7 0.037 0.074 0.496 0.620 0.027 0.027 WFsi7 0.546 0.071 7.669 0.000 0.552 0.552 WFhyp12 ~
WFhyp10 0.733 0.035 21.099 0.000 0.682 0.682 WFsi10 -0.047 0.038 -1.238 0.216 -0.053 -0.053 WFsi12 ~
WFhyp10 0.089 0.057 1.557 0.120 0.069 0.069 WFsi10 0.756 0.045 16.618 0.000 0.717 0.717

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.018 0.021 0.891 0.373 0.090 0.090 .WFhyp7 ~~
.WFsi7 0.058 0.012 4.765 0.000 0.288 0.288 .WFhyp10 ~~
.WFsi10 0.055 0.012 4.650 0.000 0.263 0.263 .WFhyp12 ~~
.WFsi12 0.067 0.010 6.441 0.000 0.416 0.416 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.333 0.020 16.456 0.000 0.728 0.728 RIhyp3 0.207 0.022 9.533 0.000 0.483 0.483 RIhyp4 0.287 0.021 13.366 0.000 0.638 0.638 RIhyp5 0.341 0.021 16.282 0.000 0.673 0.673 RIhyp6 0.364 0.021 17.001 0.000 0.708 0.708 RIhyp7 0.354 0.021 16.476 0.000 0.696 0.696 RIhyp8 0.342 0.019 17.940 0.000 0.661 0.661 RIhyp9 0.240 0.018 13.003 0.000 0.453 0.453 RIsi1 0.153 0.025 6.045 0.000 0.407 0.407 RIsi2 0.287 0.026 11.062 0.000 0.726 0.726 RIsi3 0.220 0.025 8.925 0.000 0.600 0.600 RIsi4 0.036 0.022 1.691 0.091 0.074 0.074 RIsi5 0.339 0.027 12.377 0.000 0.743 0.743 RIsi6 0.194 0.026 7.409 0.000 0.434 0.434 RIhyp2 ~~
RIhyp3 0.248 0.023 10.855 0.000 0.613 0.613 RIhyp4 0.326 0.022 14.639 0.000 0.770 0.770 RIhyp5 0.255 0.022 11.553 0.000 0.534 0.534 RIhyp6 0.288 0.023 12.729 0.000 0.595 0.595 RIhyp7 0.269 0.023 11.853 0.000 0.562 0.562 RIhyp8 0.291 0.020 14.621 0.000 0.597 0.597 RIhyp9 0.315 0.018 17.313 0.000 0.629 0.629 RIsi1 0.147 0.026 5.637 0.000 0.414 0.414 RIsi2 0.225 0.028 8.003 0.000 0.603 0.603 RIsi3 0.202 0.026 7.728 0.000 0.583 0.583 RIsi4 0.034 0.021 1.573 0.116 0.073 0.073 RIsi5 0.295 0.029 10.348 0.000 0.687 0.687 RIsi6 0.143 0.027 5.360 0.000 0.341 0.341 RIhyp3 ~~
RIhyp4 0.309 0.024 12.935 0.000 0.778 0.778 RIhyp5 0.147 0.024 6.206 0.000 0.330 0.330 RIhyp6 0.181 0.024 7.468 0.000 0.399 0.399 RIhyp7 0.156 0.024 6.493 0.000 0.348 0.348 RIhyp8 0.280 0.021 13.519 0.000 0.615 0.615 RIhyp9 0.303 0.019 16.218 0.000 0.645 0.645 RIsi1 0.107 0.027 3.891 0.000 0.321 0.321 RIsi2 0.144 0.030 4.715 0.000 0.412 0.412 RIsi3 0.146 0.028 5.234 0.000 0.449 0.449 RIsi4 0.010 0.022 0.459 0.646 0.024 0.024 RIsi5 0.189 0.031 6.156 0.000 0.469 0.469 RIsi6 0.036 0.029 1.244 0.213 0.091 0.091 RIhyp4 ~~
RIhyp5 0.237 0.023 10.171 0.000 0.506 0.506 RIhyp6 0.296 0.024 12.331 0.000 0.621 0.621 RIhyp7 0.302 0.024 12.520 0.000 0.641 0.641 RIhyp8 0.320 0.021 15.538 0.000 0.668 0.668 RIhyp9 0.265 0.019 14.230 0.000 0.538 0.538 RIsi1 0.148 0.027 5.497 0.000 0.425 0.425 RIsi2 0.266 0.029 9.136 0.000 0.727 0.727 RIsi3 0.213 0.027 7.879 0.000 0.625 0.625 RIsi4 0.036 0.022 1.622 0.105 0.079 0.079 RIsi5 0.296 0.030 9.957 0.000 0.701 0.701 RIsi6 0.146 0.028 5.234 0.000 0.353 0.353 RIhyp5 ~~
RIhyp6 0.531 0.022 23.676 0.000 0.990 0.990 RIhyp7 0.427 0.023 18.593 0.000 0.805 0.805 RIhyp8 0.381 0.020 18.937 0.000 0.708 0.708 RIhyp9 0.213 0.020 10.820 0.000 0.384 0.384 RIsi1 0.146 0.027 5.427 0.000 0.372 0.372 RIsi2 0.238 0.029 8.241 0.000 0.576 0.576 RIsi3 0.153 0.026 5.776 0.000 0.399 0.399 RIsi4 0.075 0.022 3.327 0.001 0.146 0.146 RIsi5 0.262 0.030 8.855 0.000 0.552 0.552 RIsi6 0.200 0.027 7.293 0.000 0.430 0.430 RIhyp6 ~~
RIhyp7 0.534 0.023 22.979 0.000 0.991 0.991 RIhyp8 0.471 0.020 23.382 0.000 0.859 0.859 RIhyp9 0.248 0.020 12.230 0.000 0.439 0.439 RIsi1 0.144 0.028 5.117 0.000 0.360 0.360 RIsi2 0.271 0.030 9.030 0.000 0.646 0.646 RIsi3 0.158 0.028 5.700 0.000 0.404 0.404 RIsi4 0.086 0.023 3.708 0.000 0.165 0.165 RIsi5 0.294 0.031 9.432 0.000 0.608 0.608 RIsi6 0.217 0.029 7.604 0.000 0.459 0.459 RIhyp7 ~~
RIhyp8 0.442 0.021 21.244 0.000 0.817 0.817 RIhyp9 0.215 0.021 10.279 0.000 0.386 0.386 RIsi1 0.176 0.028 6.225 0.000 0.446 0.446 RIsi2 0.255 0.030 8.606 0.000 0.615 0.615 RIsi3 0.175 0.027 6.411 0.000 0.455 0.455 RIsi4 0.090 0.023 3.852 0.000 0.176 0.176 RIsi5 0.283 0.031 9.226 0.000 0.592 0.592 RIsi6 0.229 0.028 8.055 0.000 0.489 0.489 RIhyp8 ~~
RIhyp9 0.315 0.018 17.497 0.000 0.555 0.555 RIsi1 0.097 0.025 3.928 0.000 0.243 0.243 RIsi2 0.197 0.025 7.729 0.000 0.467 0.467 RIsi3 0.153 0.024 6.457 0.000 0.390 0.390 RIsi4 0.025 0.022 1.176 0.240 0.049 0.049 RIsi5 0.224 0.028 8.058 0.000 0.462 0.462 RIsi6 0.112 0.027 4.212 0.000 0.235 0.235 RIhyp9 ~~
RIsi1 0.114 0.023 5.020 0.000 0.277 0.277 RIsi2 0.114 0.024 4.800 0.000 0.262 0.262 RIsi3 0.150 0.021 7.092 0.000 0.371 0.371 RIsi4 -0.027 0.020 -1.342 0.180 -0.050 -0.050 RIsi5 0.161 0.026 6.132 0.000 0.321 0.321 RIsi6 -0.030 0.025 -1.218 0.223 -0.062 -0.062 RIsi1 ~~
RIsi2 0.120 0.053 2.269 0.023 0.391 0.391 RIsi3 0.159 0.049 3.231 0.001 0.556 0.556 RIsi4 0.119 0.037 3.255 0.001 0.315 0.315 RIsi5 0.142 0.056 2.556 0.011 0.401 0.401 RIsi6 0.174 0.048 3.635 0.000 0.501 0.501 RIsi2 ~~
RIsi3 0.046 0.054 0.869 0.385 0.155 0.155 RIsi4 0.199 0.040 5.018 0.000 0.499 0.499 RIsi5 0.359 0.062 5.816 0.000 0.966 0.966 RIsi6 0.243 0.054 4.498 0.000 0.667 0.667 RIsi3 ~~
RIsi4 -0.023 0.035 -0.641 0.522 -0.061 -0.061 RIsi5 0.066 0.056 1.168 0.243 0.191 0.191 RIsi6 0.043 0.048 0.904 0.366 0.127 0.127 RIsi4 ~~
RIsi5 0.176 0.043 4.112 0.000 0.382 0.382 RIsi6 0.375 0.037 10.069 0.000 0.834 0.834 RIsi5 ~~
RIsi6 0.249 0.057 4.387 0.000 0.592 0.592

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 (p) -0.062 0.010 -6.138 0.000 -0.062 -0.062 .pe84m7 (p) -0.062 0.010 -6.138 0.000 -0.062 -0.062 .pe84m10 (p) -0.062 0.010 -6.138 0.000 -0.062 -0.062 .pe84m12 (p) -0.062 0.010 -6.138 0.000 -0.062 -0.062 .pe85m5 (q) -0.010 0.009 -1.043 0.297 -0.010 -0.010 .pe85m7 (q) -0.010 0.009 -1.043 0.297 -0.010 -0.010 .pe85m10 (q) -0.010 0.009 -1.043 0.297 -0.010 -0.010 .pe85m12 (q) -0.010 0.009 -1.043 0.297 -0.010 -0.010 .pe96m5 (r) -0.009 0.010 -0.953 0.341 -0.009 -0.009 .pe96m7 (r) -0.009 0.010 -0.953 0.341 -0.009 -0.009 .pe96m10 (r) -0.009 0.010 -0.953 0.341 -0.009 -0.009 .pe96m12 (r) -0.009 0.010 -0.953 0.341 -0.009 -0.009 .pe97m5 (s) 0.017 0.009 1.860 0.063 0.017 0.017 .pe97m7 (s) 0.017 0.009 1.860 0.063 0.017 0.017 .pe97m10 (s) 0.017 0.009 1.860 0.063 0.017 0.017 .pe97m12 (s) 0.017 0.009 1.860 0.063 0.017 0.017 .pe92m5 (t) -0.022 0.010 -2.171 0.030 -0.022 -0.022 .pe92m7 (t) -0.022 0.010 -2.171 0.030 -0.022 -0.022 .pe92m10 (t) -0.022 0.010 -2.171 0.030 -0.022 -0.022 .pe92m12 (t) -0.022 0.010 -2.171 0.030 -0.022 -0.022 .pe93m5 (u) -0.036 0.010 -3.626 0.000 -0.036 -0.036 .pe93m7 (u) -0.036 0.010 -3.626 0.000 -0.036 -0.036 .pe93m10 (u) -0.036 0.010 -3.626 0.000 -0.036 -0.036 .pe93m12 (u) -0.036 0.010 -3.626 0.000 -0.036 -0.036 .pe94m5 (v) -0.018 0.012 -1.531 0.126 -0.018 -0.018 .pe94m7 (v) -0.018 0.012 -1.531 0.126 -0.018 -0.018 .pe94m10 (v) -0.018 0.012 -1.531 0.126 -0.018 -0.018 .pe94m12 (v) -0.018 0.012 -1.531 0.126 -0.018 -0.018 .pe95m5 (w) -0.006 0.010 -0.626 0.531 -0.006 -0.006 .pe95m7 (w) -0.006 0.010 -0.626 0.531 -0.006 -0.006 .pe95m10 (w) -0.006 0.010 -0.626 0.531 -0.006 -0.006 .pe95m12 (w) -0.006 0.010 -0.626 0.531 -0.006 -0.006 .pe64m5 (x) -0.005 0.011 -0.484 0.628 -0.005 -0.005 .pe64m7 (x) -0.005 0.011 -0.484 0.628 -0.005 -0.005 .pe64m10 (x) -0.005 0.011 -0.484 0.628 -0.005 -0.005 .pe64m12 (x) -0.005 0.011 -0.484 0.628 -0.005 -0.005 .pe2m5 (y) 0.052 0.015 3.369 0.001 0.052 0.052 .pe2m7 (y) 0.052 0.015 3.369 0.001 0.052 0.052 .pe2m10 (y) 0.052 0.015 3.369 0.001 0.052 0.052 .pe2m12 (y) 0.052 0.015 3.369 0.001 0.052 0.052 .pe4m5 (z) 0.053 0.014 3.736 0.000 0.053 0.053 .pe4m7 (z) 0.053 0.014 3.736 0.000 0.053 0.053 .pe4m10 (z) 0.053 0.014 3.736 0.000 0.053 0.053 .pe4m12 (z) 0.053 0.014 3.736 0.000 0.053 0.053 .pe7m5 (aa) -0.004 0.014 -0.261 0.794 -0.004 -0.004 .pe7m7 (aa) -0.004 0.014 -0.261 0.794 -0.004 -0.004 .pe7m10 (aa) -0.004 0.014 -0.261 0.794 -0.004 -0.004 .pe7m12 (aa) -0.004 0.014 -0.261 0.794 -0.004 -0.004 .pe11m5 (ab) 0.023 0.015 1.509 0.131 0.023 0.023 .pe11m7 (ab) 0.023 0.015 1.509 0.131 0.023 0.023 .pe11m10 (ab) 0.023 0.015 1.509 0.131 0.023 0.023 .pe11m12 (ab) 0.023 0.015 1.509 0.131 0.023 0.023 .pe13m5 (ac) 0.009 0.021 0.453 0.650 0.009 0.009 .pe13m7 (ac) 0.009 0.021 0.453 0.650 0.009 0.009 .pe13m10 (ac) 0.009 0.021 0.453 0.650 0.009 0.009 .pe13m12 (ac) 0.009 0.021 0.453 0.650 0.009 0.009 .pe25m5 (ad) 0.007 0.018 0.411 0.681 0.007 0.007 .pe25m7 (ad) 0.007 0.018 0.411 0.681 0.007 0.007 .pe25m10 (ad) 0.007 0.018 0.411 0.681 0.007 0.007 .pe25m12 (ad) 0.007 0.018 0.411 0.681 0.007 0.007 .WFhyp7 0.015 0.013 1.152 0.249 0.034 0.034 .WFhyp10 0.009 0.013 0.667 0.505 0.018 0.018 .WFhyp12 0.003 0.012 0.247 0.805 0.006 0.006 .WFsi7 -0.023 0.024 -0.940 0.347 -0.038 -0.038 .WFsi10 -0.014 0.021 -0.660 0.509 -0.024 -0.024 .WFsi12 -0.006 0.021 -0.282 0.778 -0.010 -0.010 RIhyp1 0.000 0.000 0.000 RIhyp2 0.000 0.000 0.000 RIhyp3 0.000 0.000 0.000 RIhyp4 0.000 0.000 0.000 RIhyp5 0.000 0.000 0.000 RIhyp6 0.000 0.000 0.000 RIhyp7 0.000 0.000 0.000 RIhyp8 0.000 0.000 0.000 RIhyp9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe84m5|t1 -0.063 0.022 -2.933 0.003 -0.063 -0.063 pe84m5|t2 0.911 0.026 34.741 0.000 0.911 0.911 pe84m7|t1 0.030 0.020 1.502 0.133 0.030 0.030 pe84m7|t2 1.185 0.027 44.654 0.000 1.185 1.185 pe84m10|t1 0.066 0.021 3.185 0.001 0.066 0.066 pe84m10|t2 1.211 0.026 46.331 0.000 1.211 1.211 pe84m12|t1 0.045 0.021 2.151 0.031 0.045 0.045 pe84m12|t2 1.249 0.027 46.940 0.000 1.249 1.249 pe85m5|t1 -0.941 0.026 -35.617 0.000 -0.941 -0.941 pe85m5|t2 0.257 0.023 11.373 0.000 0.257 0.257 pe85m7|t1 -0.859 0.025 -34.252 0.000 -0.859 -0.859 pe85m7|t2 0.593 0.020 29.109 0.000 0.593 0.593 pe85m10|t1 -0.472 0.022 -21.645 0.000 -0.472 -0.472 pe85m10|t2 0.872 0.022 39.495 0.000 0.872 0.872 pe85m12|t1 -0.282 0.021 -13.138 0.000 -0.282 -0.282 pe85m12|t2 0.958 0.023 42.422 0.000 0.958 0.958 pe96m5|t1 -0.197 0.022 -8.890 0.000 -0.197 -0.197 pe96m5|t2 0.840 0.025 33.116 0.000 0.840 0.840 pe96m7|t1 -0.160 0.021 -7.579 0.000 -0.160 -0.160 pe96m7|t2 0.932 0.023 41.316 0.000 0.932 0.932 pe96m10|t1 -0.026 0.020 -1.301 0.193 -0.026 -0.026 pe96m10|t2 1.082 0.024 45.858 0.000 1.082 1.082 pe96m12|t1 0.066 0.021 3.197 0.001 0.066 0.066 pe96m12|t2 1.127 0.023 49.827 0.000 1.127 1.127 pe97m5|t1 -0.341 0.022 -15.160 0.000 -0.341 -0.341 pe97m5|t2 0.623 0.024 26.413 0.000 0.623 0.623 pe97m7|t1 -0.234 0.021 -11.190 0.000 -0.234 -0.234 pe97m7|t2 0.826 0.021 39.855 0.000 0.826 0.826 pe97m10|t1 0.174 0.020 8.777 0.000 0.174 0.174 pe97m10|t2 1.145 0.023 49.226 0.000 1.145 1.145 pe97m12|t1 0.291 0.020 14.665 0.000 0.291 0.291 pe97m12|t2 1.204 0.023 52.598 0.000 1.204 1.204 pe92m5|t1 -0.017 0.021 -0.805 0.421 -0.017 -0.017 pe92m5|t2 0.788 0.024 32.401 0.000 0.788 0.788 pe92m7|t1 0.053 0.019 2.786 0.005 0.053 0.053 pe92m7|t2 0.954 0.021 44.536 0.000 0.954 0.954 pe92m10|t1 0.361 0.019 18.963 0.000 0.361 0.361 pe92m10|t2 1.148 0.023 50.434 0.000 1.148 1.148 pe92m12|t1 0.400 0.019 20.741 0.000 0.400 0.400 pe92m12|t2 1.243 0.024 51.383 0.000 1.243 1.243 pe93m5|t1 0.162 0.021 7.584 0.000 0.162 0.162 pe93m5|t2 0.838 0.024 34.621 0.000 0.838 0.838 pe93m7|t1 0.336 0.018 18.596 0.000 0.336 0.336 pe93m7|t2 0.983 0.021 47.896 0.000 0.983 0.983 pe93m10|t1 0.498 0.018 27.640 0.000 0.498 0.498 pe93m10|t2 1.220 0.024 51.849 0.000 1.220 1.220 pe93m12|t1 0.578 0.019 30.928 0.000 0.578 0.578 pe93m12|t2 1.279 0.024 54.119 0.000 1.279 1.279 pe94m5|t1 0.566 0.023 24.984 0.000 0.566 0.566 pe94m5|t2 1.212 0.028 42.736 0.000 1.212 1.212 pe94m7|t1 0.716 0.020 36.691 0.000 0.716 0.716 pe94m7|t2 1.373 0.027 51.034 0.000 1.373 1.373 pe94m10|t1 0.813 0.020 39.755 0.000 0.813 0.813 pe94m10|t2 1.554 0.031 49.752 0.000 1.554 1.554 pe94m12|t1 0.888 0.020 43.401 0.000 0.888 0.888 pe94m12|t2 1.587 0.031 50.951 0.000 1.587 1.587 pe95m5|t1 -0.012 0.021 -0.573 0.567 -0.012 -0.012 pe95m5|t2 0.503 0.022 22.999 0.000 0.503 0.503 pe95m7|t1 0.155 0.019 8.187 0.000 0.155 0.155 pe95m7|t2 0.773 0.020 38.660 0.000 0.773 0.773 pe95m10|t1 0.294 0.019 15.732 0.000 0.294 0.294 pe95m10|t2 0.947 0.022 43.265 0.000 0.947 0.947 pe95m12|t1 0.472 0.018 25.593 0.000 0.472 0.472 pe95m12|t2 1.086 0.023 46.596 0.000 1.086 1.086 pe64m5|t1 -0.251 0.021 -11.934 0.000 -0.251 -0.251 pe64m5|t2 0.485 0.022 22.242 0.000 0.485 0.485 pe64m7|t1 -0.278 0.021 -13.431 0.000 -0.278 -0.278 pe64m7|t2 0.640 0.021 29.868 0.000 0.640 0.640 pe64m10|t1 -0.069 0.020 -3.456 0.001 -0.069 -0.069 pe64m10|t2 0.875 0.023 38.477 0.000 0.875 0.875 pe64m12|t1 -0.041 0.020 -2.057 0.040 -0.041 -0.041 pe64m12|t2 0.877 0.023 38.796 0.000 0.877 0.877 pe2m5|t1 1.417 0.033 42.942 0.000 1.417 1.417 pe2m5|t2 2.419 0.074 32.761 0.000 2.419 2.419 pe2m7|t1 1.205 0.032 37.344 0.000 1.205 1.205 pe2m7|t2 2.288 0.058 39.355 0.000 2.288 2.288 pe2m10|t1 1.032 0.030 33.944 0.000 1.032 1.032 pe2m10|t2 2.161 0.053 40.989 0.000 2.161 2.161 pe2m12|t1 1.157 0.031 37.558 0.000 1.157 1.157 pe2m12|t2 2.266 0.057 39.639 0.000 2.266 2.266 pe4m5|t1 1.060 0.027 39.250 0.000 1.060 1.060 pe4m5|t2 2.183 0.057 38.476 0.000 2.183 2.183 pe4m7|t1 1.039 0.032 32.815 0.000 1.039 1.039 pe4m7|t2 2.286 0.054 42.234 0.000 2.286 2.286 pe4m10|t1 0.940 0.031 30.750 0.000 0.940 0.940 pe4m10|t2 2.170 0.046 46.738 0.000 2.170 2.170 pe4m12|t1 0.927 0.033 28.318 0.000 0.927 0.927 pe4m12|t2 2.249 0.051 44.125 0.000 2.249 2.249 pe7m5|t1 0.830 0.026 31.576 0.000 0.830 0.830 pe7m5|t2 1.954 0.050 39.443 0.000 1.954 1.954 pe7m7|t1 0.633 0.028 22.389 0.000 0.633 0.633 pe7m7|t2 1.803 0.041 43.749 0.000 1.803 1.803 pe7m10|t1 0.619 0.028 22.241 0.000 0.619 0.619 pe7m10|t2 1.908 0.044 43.070 0.000 1.908 1.908 pe7m12|t1 0.687 0.028 24.372 0.000 0.687 0.687 pe7m12|t2 1.976 0.047 41.765 0.000 1.976 1.976 pe11m5|t1 0.712 0.025 28.700 0.000 0.712 0.712 pe11m5|t2 1.807 0.042 43.242 0.000 1.807 1.807 pe11m7|t1 0.866 0.027 32.545 0.000 0.866 0.866 pe11m7|t2 1.897 0.042 45.318 0.000 1.897 1.897 pe11m10|t1 0.800 0.026 30.608 0.000 0.800 0.800 pe11m10|t2 1.985 0.046 42.784 0.000 1.985 1.985 pe11m12|t1 0.870 0.027 32.225 0.000 0.870 0.870 pe11m12|t2 2.004 0.047 42.427 0.000 2.004 2.004 pe13m5|t1 1.586 0.037 43.378 0.000 1.586 1.586 pe13m5|t2 2.658 0.099 26.848 0.000 2.658 2.658 pe13m7|t1 1.441 0.037 38.976 0.000 1.441 1.441 pe13m7|t2 2.589 0.082 31.451 0.000 2.589 2.589 pe13m10|t1 1.233 0.035 35.350 0.000 1.233 1.233 pe13m10|t2 2.371 0.062 38.105 0.000 2.371 2.371 pe13m12|t1 1.180 0.035 33.680 0.000 1.180 1.180 pe13m12|t2 2.265 0.056 40.310 0.000 2.265 2.265 pe25m5|t1 1.179 0.030 39.272 0.000 1.179 1.179 pe25m5|t2 2.291 0.066 34.828 0.000 2.291 2.291 pe25m7|t1 1.369 0.034 39.982 0.000 1.369 1.369 pe25m7|t2 2.308 0.061 37.790 0.000 2.308 2.308 pe25m10|t1 1.350 0.031 43.076 0.000 1.350 1.350 pe25m10|t2 2.315 0.063 36.515 0.000 2.315 2.315 pe25m12|t1 1.239 0.032 38.250 0.000 1.239 1.239 pe25m12|t2 2.441 0.074 32.995 0.000 2.441 2.441

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 0.360 0.360 0.360 .pe84m7 0.329 0.329 0.329 .pe84m10 0.285 0.285 0.285 .pe84m12 0.249 0.249 0.249 .pe85m5 0.373 0.373 0.373 .pe85m7 0.334 0.334 0.334 .pe85m10 0.279 0.279 0.279 .pe85m12 0.234 0.234 0.234 .pe96m5 0.374 0.374 0.374 .pe96m7 0.325 0.325 0.325 .pe96m10 0.255 0.255 0.255 .pe96m12 0.199 0.199 0.199 .pe97m5 0.359 0.359 0.359 .pe97m7 0.314 0.314 0.314 .pe97m10 0.250 0.250 0.250 .pe97m12 0.199 0.199 0.199 .pe92m5 0.287 0.287 0.287 .pe92m7 0.250 0.250 0.250 .pe92m10 0.197 0.197 0.197 .pe92m12 0.155 0.155 0.155 .pe93m5 0.256 0.256 0.256 .pe93m7 0.216 0.216 0.216 .pe93m10 0.160 0.160 0.160 .pe93m12 0.115 0.115 0.115 .pe94m5 0.284 0.284 0.284 .pe94m7 0.248 0.248 0.248 .pe94m10 0.197 0.197 0.197 .pe94m12 0.155 0.155 0.155 .pe95m5 0.331 0.331 0.331 .pe95m7 0.307 0.307 0.307 .pe95m10 0.274 0.274 0.274 .pe95m12 0.246 0.246 0.246 .pe64m5 0.334 0.334 0.334 .pe64m7 0.318 0.318 0.318 .pe64m10 0.295 0.295 0.295 .pe64m12 0.276 0.276 0.276 .pe2m5 0.434 0.434 0.434 .pe2m7 0.356 0.356 0.356 .pe2m10 0.363 0.363 0.363 .pe2m12 0.325 0.325 0.325 .pe4m5 0.305 0.305 0.305 .pe4m7 0.199 0.199 0.199 .pe4m10 0.208 0.208 0.208 .pe4m12 0.156 0.156 0.156 .pe7m5 0.404 0.404 0.404 .pe7m7 0.314 0.314 0.314 .pe7m10 0.322 0.322 0.322 .pe7m12 0.277 0.277 0.277 .pe11m5 0.380 0.380 0.380 .pe11m7 0.344 0.344 0.344 .pe11m10 0.348 0.348 0.348 .pe11m12 0.330 0.330 0.330 .pe13m5 0.231 0.231 0.231 .pe13m7 0.135 0.135 0.135 .pe13m10 0.144 0.144 0.144 .pe13m12 0.096 0.096 0.096 .pe25m5 0.321 0.321 0.321 .pe25m7 0.245 0.245 0.245 .pe25m10 0.252 0.252 0.252 .pe25m12 0.215 0.215 0.215 RIhyp1 0.485 0.020 24.117 0.000 1.000 1.000 RIhyp2 0.431 0.022 19.375 0.000 1.000 1.000 RIhyp3 0.378 0.025 14.830 0.000 1.000 1.000 RIhyp4 0.417 0.025 16.819 0.000 1.000 1.000 RIhyp5 0.528 0.022 23.715 0.000 1.000 1.000 RIhyp6 0.546 0.024 22.800 0.000 1.000 1.000 RIhyp7 0.533 0.025 21.637 0.000 1.000 1.000 RIhyp8 0.551 0.018 29.865 0.000 1.000 1.000 RIhyp9 0.583 0.015 38.727 0.000 1.000 1.000 RIsi1 0.292 0.052 5.609 0.000 1.000 1.000 RIsi2 0.322 0.062 5.181 0.000 1.000 1.000 RIsi3 0.279 0.052 5.362 0.000 1.000 1.000 RIsi4 0.493 0.030 16.313 0.000 1.000 1.000 RIsi5 0.429 0.064 6.748 0.000 1.000 1.000 RIsi6 0.411 0.052 7.881 0.000 1.000 1.000 WFhyp5 0.156 0.017 8.922 0.000 1.000 1.000 .WFhyp7 0.165 0.014 11.896 0.000 0.886 0.886 .WFhyp10 0.184 0.015 12.544 0.000 0.799 0.799 .WFhyp12 0.147 0.012 12.161 0.000 0.552 0.552 WFsi5 0.274 0.054 5.052 0.000 1.000 1.000 .WFsi7 0.249 0.033 7.495 0.000 0.708 0.708 .WFsi10 0.237 0.031 7.586 0.000 0.688 0.688 .WFsi12 0.174 0.026 6.791 0.000 0.454 0.454

S3 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S2 model) Comparative Fit Index (CFI) 0.986 (>0.95) Change in CFI: 0.000 (increase) - same fit Tucker-Lewis Index (TLI) 0.985 (>0.95) Change in TLI: 0.000 (increase) - same fit RMSEA 0.019 (≤ 0.06) Change in RMSEA: 0.000 (decrease) - same fit 90 Percent confidence interval - lower 0.018 90 Percent confidence interval - upper 0.021
SRMR 0.040 (≤ 0.08) Change in SRMR: 0.006 (increase) - same fit

# summary(semTools::compareFit(RICLPM_multi_hyp_S2.fit, RICLPM_multi_hyp_S3.fit, nested = TRUE)) #† indicates the best fitting model - hashed out to keep script running

Again this function does not run - perhaps there is something wrong with the way we are running the S3 code.

But the model shows almost no change in model fit from S2 - so we assume strong measurement invariance.

# Table of model fit 
RICLPM_multi_hyp_S3.fit.summary.fit <- table.model.fit(RICLPM_multi_hyp_S3.fit.summary)
# Table of regression coefficients and covariances 
RICLPM_multi_hyp_S3.fit.summary.reg <- table.model.coef(model = RICLPM_multi_hyp_S3.fit.summary, step = "S3")

RICLPM_multi_hyp_S4: Hyperactivity step 4 - Full model

RICLPM_multi_hyp_S4 <- '
  
  #####################
  # MEASUREMENT MODEL #
  #####################
  
  # Factor models for hyptention symptoms at 4 waves (constrained)
  Fhyp5 =~ a*pe84m5 + b*pe85m5 + c*pe96m5 + d*pe97m5 + e*pe92m5 + f*pe93m5 + g*pe94m5 + h*pe95m5 + i*pe64m5
  Fhyp7 =~ a*pe84m7 + b*pe85m7 + c*pe96m7 + d*pe97m7 + e*pe92m7 + f*pe93m7 + g*pe94m7 + h*pe95m7 + i*pe64m7
  Fhyp10 =~ a*pe84m10 + b*pe85m10 + c*pe96m10 + d*pe97m10 + e*pe92m10 + f*pe93m10 + g*pe94m10 + h*pe95m10 + i*pe64m10
  Fhyp12 =~ a*pe84m12 + b*pe85m12 + c*pe96m12 + d*pe97m12 + e*pe92m12 + f*pe93m12 + g*pe94m12 + h*pe95m12 + i*pe64m12 
  
  # Factor models for social isolation at 4 waves (constrained)
  Fsi5 =~ j*pe2m5 + k*pe4m5 + l*pe7m5 + m*pe11m5 + n*pe13m5 + o*pe25m5 
  Fsi7 =~ j*pe2m7 + k*pe4m7 + l*pe7m7 + m*pe11m7 + n*pe13m7 + o*pe25m7 
  Fsi10 =~ j*pe2m10 + k*pe4m10 + l*pe7m10 + m*pe11m10 + n*pe13m10 + o*pe25m10
  Fsi12 =~ j*pe2m12 + k*pe4m12 + l*pe7m12 + m*pe11m12 + n*pe13m12 + o*pe25m12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  pe84m5 + pe84m7 + pe84m10 + pe84m12 ~ p*1
  pe85m5 + pe85m7 + pe85m10 + pe85m12 ~ q*1
  pe96m5 + pe96m7 + pe96m10 + pe96m12 ~ r*1
  pe97m5 + pe97m7 + pe97m10 + pe97m12 ~ s*1
  pe92m5 + pe92m7 + pe92m10 + pe92m12 ~ t*1
  pe93m5 + pe93m7 + pe93m10 + pe93m12 ~ u*1
  pe94m5 + pe94m7 + pe94m10 + pe94m12 ~ v*1
  pe95m5 + pe95m7 + pe95m10 + pe95m12 ~ w*1
  pe64m5 + pe64m7 + pe64m10 + pe64m12 ~ x*1
  
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ y*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ z*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ aa*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ ab*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ ac*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ ad*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  Fhyp7 + Fhyp10 + Fhyp12 + Fsi7 + Fsi10 + Fsi12 ~ 1
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) 
  RIhyp =~ 1*Fhyp5 + 1*Fhyp7 + 1*Fhyp10 + 1*Fhyp12
  RIsi =~ 1*Fsi5 + 1*Fsi7 + 1*Fsi10 + 1*Fsi12
  
  # Set the residual variances of all Fhyp and Fsi variables to 0. 
  Fhyp5 ~~ 0*Fhyp5
  Fhyp7 ~~ 0*Fhyp7
  Fhyp10 ~~ 0*Fhyp10
  Fhyp12 ~~ 0*Fhyp12
  Fsi5 ~~ 0*Fsi5
  Fsi7 ~~ 0*Fsi7
  Fsi10 ~~ 0*Fsi10
  Fsi12 ~~ 0*Fsi12
  
  ###############
  # WITHIN PART #
  ###############
  
  # Create the within-part
  WFhyp5 =~ 1*Fhyp5
  WFhyp7 =~ 1*Fhyp7
  WFhyp10 =~ 1*Fhyp10
  WFhyp12 =~ 1*Fhyp12
  
  WFsi5 =~ 1*Fsi5
  WFsi7 =~ 1*Fsi7
  WFsi10 =~ 1*Fsi10
  WFsi12 =~ 1*Fsi12
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Set correlations between the between-factors (random intercepts) and within-factors at wave 1 (age 5) at 0
  RIhyp + RIsi ~~ 0*WFhyp5 + 0*WFsi5
'

RICLPM_multi_hyp_S4.fit <- cfa(RICLPM_multi_hyp_S4, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPM_multi_hyp_S4.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 103 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 265 Number of equality constraints 84

Number of observations 2232 Number of missing patterns 43

Model Test User Model: Standard Robust Test Statistic 17780.062 10652.025 Degrees of freedom 1709 1709 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.839 Shift parameter 985.696 simple second-order correction

Model Test Baseline Model:

Test statistic 361279.045 95505.953 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 3.835

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.955 0.905 Tucker-Lewis Index (TLI) 0.954 0.901

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.065 0.048 90 Percent confidence interval - lower 0.064 0.048 90 Percent confidence interval - upper 0.066 0.049 P-value RMSEA <= 0.05 0.000 0.998

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.086 0.086

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Fhyp5 =~
pe84m5 (a) 1.000 0.698 0.698 pe85m5 (b) 0.984 0.015 65.261 0.000 0.687 0.687 pe96m5 (c) 0.888 0.018 50.322 0.000 0.620 0.620 pe97m5 (d) 1.012 0.016 63.810 0.000 0.706 0.706 pe92m5 (e) 1.089 0.016 68.578 0.000 0.760 0.760 pe93m5 (f) 1.200 0.016 72.851 0.000 0.837 0.837 pe94m5 (g) 1.117 0.017 64.463 0.000 0.780 0.780 pe95m5 (h) 1.044 0.017 60.647 0.000 0.729 0.729 pe64m5 (i) 0.852 0.021 40.311 0.000 0.594 0.594 Fhyp7 =~
pe84m7 (a) 1.000 0.721 0.721 pe85m7 (b) 0.984 0.015 65.261 0.000 0.709 0.709 pe96m7 (c) 0.888 0.018 50.322 0.000 0.640 0.640 pe97m7 (d) 1.012 0.016 63.810 0.000 0.730 0.730 pe92m7 (e) 1.089 0.016 68.578 0.000 0.785 0.785 pe93m7 (f) 1.200 0.016 72.851 0.000 0.865 0.865 pe94m7 (g) 1.117 0.017 64.463 0.000 0.806 0.806 pe95m7 (h) 1.044 0.017 60.647 0.000 0.753 0.753 pe64m7 (i) 0.852 0.021 40.311 0.000 0.614 0.614 Fhyp10 =~
pe84m10 (a) 1.000 0.751 0.751 pe85m10 (b) 0.984 0.015 65.261 0.000 0.739 0.739 pe96m10 (c) 0.888 0.018 50.322 0.000 0.667 0.667 pe97m10 (d) 1.012 0.016 63.810 0.000 0.760 0.760 pe92m10 (e) 1.089 0.016 68.578 0.000 0.818 0.818 pe93m10 (f) 1.200 0.016 72.851 0.000 0.901 0.901 pe94m10 (g) 1.117 0.017 64.463 0.000 0.839 0.839 pe95m10 (h) 1.044 0.017 60.647 0.000 0.785 0.785 pe64m10 (i) 0.852 0.021 40.311 0.000 0.640 0.640 Fhyp12 =~
pe84m12 (a) 1.000 0.775 0.775 pe85m12 (b) 0.984 0.015 65.261 0.000 0.763 0.763 pe96m12 (c) 0.888 0.018 50.322 0.000 0.688 0.688 pe97m12 (d) 1.012 0.016 63.810 0.000 0.785 0.785 pe92m12 (e) 1.089 0.016 68.578 0.000 0.844 0.844 pe93m12 (f) 1.200 0.016 72.851 0.000 0.930 0.930 pe94m12 (g) 1.117 0.017 64.463 0.000 0.866 0.866 pe95m12 (h) 1.044 0.017 60.647 0.000 0.810 0.810 pe64m12 (i) 0.852 0.021 40.311 0.000 0.660 0.660 Fsi5 =~
pe2m5 (j) 1.000 0.604 0.604 pe4m5 (k) 1.342 0.046 28.887 0.000 0.811 0.811 pe7m5 (l) 1.000 0.036 27.488 0.000 0.604 0.604 pe11m5 (m) 0.817 0.041 19.744 0.000 0.493 0.493 pe13m5 (n) 1.428 0.049 29.029 0.000 0.862 0.862 pe25m5 (o) 1.173 0.046 25.558 0.000 0.709 0.709 Fsi7 =~
pe2m7 (j) 1.000 0.648 0.648 pe4m7 (k) 1.342 0.046 28.887 0.000 0.870 0.870 pe7m7 (l) 1.000 0.036 27.488 0.000 0.649 0.649 pe11m7 (m) 0.817 0.041 19.744 0.000 0.530 0.530 pe13m7 (n) 1.428 0.049 29.029 0.000 0.926 0.926 pe25m7 (o) 1.173 0.046 25.558 0.000 0.760 0.760 Fsi10 =~
pe2m10 (j) 1.000 0.644 0.644 pe4m10 (k) 1.342 0.046 28.887 0.000 0.864 0.864 pe7m10 (l) 1.000 0.036 27.488 0.000 0.644 0.644 pe11m10 (m) 0.817 0.041 19.744 0.000 0.526 0.526 pe13m10 (n) 1.428 0.049 29.029 0.000 0.919 0.919 pe25m10 (o) 1.173 0.046 25.558 0.000 0.755 0.755 Fsi12 =~
pe2m12 (j) 1.000 0.663 0.663 pe4m12 (k) 1.342 0.046 28.887 0.000 0.889 0.889 pe7m12 (l) 1.000 0.036 27.488 0.000 0.663 0.663 pe11m12 (m) 0.817 0.041 19.744 0.000 0.541 0.541 pe13m12 (n) 1.428 0.049 29.029 0.000 0.946 0.946 pe25m12 (o) 1.173 0.046 25.558 0.000 0.777 0.777 RIhyp =~
Fhyp5 1.000 0.770 0.770 Fhyp7 1.000 0.745 0.745 Fhyp10 1.000 0.715 0.715 Fhyp12 1.000 0.693 0.693 RIsi =~
Fsi5 1.000 0.397 0.397 Fsi7 1.000 0.370 0.370 Fsi10 1.000 0.372 0.372 Fsi12 1.000 0.362 0.362 WFhyp5 =~
Fhyp5 1.000 0.639 0.639 WFhyp7 =~
Fhyp7 1.000 0.667 0.667 WFhyp10 =~
Fhyp10 1.000 0.699 0.699 WFhyp12 =~
Fhyp12 1.000 0.721 0.721 WFsi5 =~
Fsi5 1.000 0.918 0.918 WFsi7 =~
Fsi7 1.000 0.929 0.929 WFsi10 =~
Fsi10 1.000 0.928 0.928 WFsi12 =~
Fsi12 1.000 0.932 0.932

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.533 0.072 7.455 0.000 0.494 0.494 WFsi5 -0.073 0.066 -1.102 0.271 -0.084 -0.084 WFsi7 ~
WFhyp5 -0.046 0.081 -0.564 0.573 -0.034 -0.034 WFsi5 0.807 0.074 10.953 0.000 0.742 0.742 WFhyp10 ~
WFhyp7 0.589 0.060 9.785 0.000 0.539 0.539 WFsi7 -0.019 0.063 -0.293 0.770 -0.021 -0.021 WFsi10 ~
WFhyp7 -0.034 0.075 -0.456 0.649 -0.028 -0.028 WFsi7 0.702 0.093 7.510 0.000 0.707 0.707 WFhyp12 ~
WFhyp10 0.822 0.031 26.132 0.000 0.772 0.772 WFsi10 -0.062 0.037 -1.690 0.091 -0.066 -0.066 WFsi12 ~
WFhyp10 0.043 0.048 0.899 0.369 0.036 0.036 WFsi10 0.877 0.046 19.229 0.000 0.849 0.849

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 -0.000 0.041 -0.005 0.996 -0.001 -0.001 .WFhyp7 ~~
.WFsi7 0.061 0.011 5.609 0.000 0.363 0.363 .WFhyp10 ~~
.WFsi10 0.048 0.010 4.602 0.000 0.253 0.253 .WFhyp12 ~~
.WFsi12 0.059 0.009 6.513 0.000 0.509 0.509 RIhyp ~~
WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIsi ~~
WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIhyp ~~
RIsi 0.165 0.040 4.152 0.000 1.278 1.278

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe84m5 (p) -0.004 0.010 -0.437 0.662 -0.004 -0.004 .pe84m7 (p) -0.004 0.010 -0.437 0.662 -0.004 -0.004 .pe84m10 (p) -0.004 0.010 -0.437 0.662 -0.004 -0.004 .pe84m12 (p) -0.004 0.010 -0.437 0.662 -0.004 -0.004 .pe85m5 (q) -0.012 0.009 -1.291 0.197 -0.012 -0.012 .pe85m7 (q) -0.012 0.009 -1.291 0.197 -0.012 -0.012 .pe85m10 (q) -0.012 0.009 -1.291 0.197 -0.012 -0.012 .pe85m12 (q) -0.012 0.009 -1.291 0.197 -0.012 -0.012 .pe96m5 (r) -0.003 0.010 -0.252 0.801 -0.003 -0.003 .pe96m7 (r) -0.003 0.010 -0.252 0.801 -0.003 -0.003 .pe96m10 (r) -0.003 0.010 -0.252 0.801 -0.003 -0.003 .pe96m12 (r) -0.003 0.010 -0.252 0.801 -0.003 -0.003 .pe97m5 (s) 0.023 0.009 2.461 0.014 0.023 0.023 .pe97m7 (s) 0.023 0.009 2.461 0.014 0.023 0.023 .pe97m10 (s) 0.023 0.009 2.461 0.014 0.023 0.023 .pe97m12 (s) 0.023 0.009 2.461 0.014 0.023 0.023 .pe92m5 (t) -0.010 0.010 -0.977 0.328 -0.010 -0.010 .pe92m7 (t) -0.010 0.010 -0.977 0.328 -0.010 -0.010 .pe92m10 (t) -0.010 0.010 -0.977 0.328 -0.010 -0.010 .pe92m12 (t) -0.010 0.010 -0.977 0.328 -0.010 -0.010 .pe93m5 (u) -0.020 0.010 -2.065 0.039 -0.020 -0.020 .pe93m7 (u) -0.020 0.010 -2.065 0.039 -0.020 -0.020 .pe93m10 (u) -0.020 0.010 -2.065 0.039 -0.020 -0.020 .pe93m12 (u) -0.020 0.010 -2.065 0.039 -0.020 -0.020 .pe94m5 (v) -0.019 0.011 -1.632 0.103 -0.019 -0.019 .pe94m7 (v) -0.019 0.011 -1.632 0.103 -0.019 -0.019 .pe94m10 (v) -0.019 0.011 -1.632 0.103 -0.019 -0.019 .pe94m12 (v) -0.019 0.011 -1.632 0.103 -0.019 -0.019 .pe95m5 (w) -0.002 0.010 -0.245 0.806 -0.002 -0.002 .pe95m7 (w) -0.002 0.010 -0.245 0.806 -0.002 -0.002 .pe95m10 (w) -0.002 0.010 -0.245 0.806 -0.002 -0.002 .pe95m12 (w) -0.002 0.010 -0.245 0.806 -0.002 -0.002 .pe64m5 (x) -0.002 0.011 -0.196 0.845 -0.002 -0.002 .pe64m7 (x) -0.002 0.011 -0.196 0.845 -0.002 -0.002 .pe64m10 (x) -0.002 0.011 -0.196 0.845 -0.002 -0.002 .pe64m12 (x) -0.002 0.011 -0.196 0.845 -0.002 -0.002 .pe2m5 (y) 0.050 0.016 3.218 0.001 0.050 0.050 .pe2m7 (y) 0.050 0.016 3.218 0.001 0.050 0.050 .pe2m10 (y) 0.050 0.016 3.218 0.001 0.050 0.050 .pe2m12 (y) 0.050 0.016 3.218 0.001 0.050 0.050 .pe4m5 (z) 0.055 0.014 3.859 0.000 0.055 0.055 .pe4m7 (z) 0.055 0.014 3.859 0.000 0.055 0.055 .pe4m10 (z) 0.055 0.014 3.859 0.000 0.055 0.055 .pe4m12 (z) 0.055 0.014 3.859 0.000 0.055 0.055 .pe7m5 (aa) 0.016 0.014 1.144 0.253 0.016 0.016 .pe7m7 (aa) 0.016 0.014 1.144 0.253 0.016 0.016 .pe7m10 (aa) 0.016 0.014 1.144 0.253 0.016 0.016 .pe7m12 (aa) 0.016 0.014 1.144 0.253 0.016 0.016 .pe11m5 (ab) 0.041 0.015 2.730 0.006 0.041 0.041 .pe11m7 (ab) 0.041 0.015 2.730 0.006 0.041 0.041 .pe11m10 (ab) 0.041 0.015 2.730 0.006 0.041 0.041 .pe11m12 (ab) 0.041 0.015 2.730 0.006 0.041 0.041 .pe13m5 (ac) 0.011 0.020 0.526 0.599 0.011 0.011 .pe13m7 (ac) 0.011 0.020 0.526 0.599 0.011 0.011 .pe13m10 (ac) 0.011 0.020 0.526 0.599 0.011 0.011 .pe13m12 (ac) 0.011 0.020 0.526 0.599 0.011 0.011 .pe25m5 (ad) 0.009 0.018 0.499 0.618 0.009 0.009 .pe25m7 (ad) 0.009 0.018 0.499 0.618 0.009 0.009 .pe25m10 (ad) 0.009 0.018 0.499 0.618 0.009 0.009 .pe25m12 (ad) 0.009 0.018 0.499 0.618 0.009 0.009 .Fhyp7 0.005 0.013 0.402 0.688 0.007 0.007 .Fhyp10 0.005 0.015 0.338 0.735 0.007 0.007 .Fhyp12 0.005 0.016 0.321 0.748 0.007 0.007 .Fsi7 -0.025 0.022 -1.142 0.254 -0.039 -0.039 .Fsi10 -0.025 0.022 -1.171 0.242 -0.039 -0.039 .Fsi12 -0.024 0.023 -1.040 0.298 -0.036 -0.036 .Fhyp5 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 RIhyp 0.000 0.000 0.000 RIsi 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 .WFhyp7 0.000 0.000 0.000 .WFhyp10 0.000 0.000 0.000 .WFhyp12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe84m5|t1 -0.006 0.022 -0.282 0.778 -0.006 -0.006 pe84m5|t2 0.968 0.026 36.909 0.000 0.968 0.968 pe85m5|t1 -0.944 0.026 -35.788 0.000 -0.944 -0.944 pe85m5|t2 0.254 0.022 11.330 0.000 0.254 0.254 pe96m5|t1 -0.190 0.022 -8.650 0.000 -0.190 -0.190 pe96m5|t2 0.847 0.025 33.478 0.000 0.847 0.847 pe97m5|t1 -0.335 0.022 -14.936 0.000 -0.335 -0.335 pe97m5|t2 0.629 0.023 26.845 0.000 0.629 0.629 pe92m5|t1 -0.005 0.021 -0.243 0.808 -0.005 -0.005 pe92m5|t2 0.800 0.024 32.896 0.000 0.800 0.800 pe93m5|t1 0.178 0.021 8.347 0.000 0.178 0.178 pe93m5|t2 0.854 0.024 35.217 0.000 0.854 0.854 pe94m5|t1 0.565 0.023 24.998 0.000 0.565 0.565 pe94m5|t2 1.211 0.028 42.654 0.000 1.211 1.211 pe95m5|t1 -0.008 0.021 -0.380 0.704 -0.008 -0.008 pe95m5|t2 0.507 0.022 23.043 0.000 0.507 0.507 pe64m5|t1 -0.248 0.021 -11.761 0.000 -0.248 -0.248 pe64m5|t2 0.489 0.022 22.263 0.000 0.489 0.489 pe84m7|t1 0.078 0.020 3.872 0.000 0.078 0.078 pe84m7|t2 1.233 0.026 46.643 0.000 1.233 1.233 pe85m7|t1 -0.872 0.025 -34.653 0.000 -0.872 -0.872 pe85m7|t2 0.579 0.021 28.198 0.000 0.579 0.579 pe96m7|t1 -0.167 0.021 -7.921 0.000 -0.167 -0.167 pe96m7|t2 0.925 0.023 39.719 0.000 0.925 0.925 pe97m7|t1 -0.240 0.021 -11.410 0.000 -0.240 -0.240 pe97m7|t2 0.820 0.021 38.622 0.000 0.820 0.820 pe92m7|t1 0.055 0.019 2.886 0.004 0.055 0.055 pe92m7|t2 0.956 0.021 45.212 0.000 0.956 0.956 pe93m7|t1 0.342 0.018 19.181 0.000 0.342 0.342 pe93m7|t2 0.989 0.020 49.559 0.000 0.989 0.989 pe94m7|t1 0.705 0.019 36.652 0.000 0.705 0.705 pe94m7|t2 1.362 0.026 51.444 0.000 1.362 1.362 pe95m7|t1 0.152 0.019 8.097 0.000 0.152 0.152 pe95m7|t2 0.770 0.019 39.657 0.000 0.770 0.770 pe64m7|t1 -0.280 0.021 -13.530 0.000 -0.280 -0.280 pe64m7|t2 0.637 0.021 29.805 0.000 0.637 0.637 pe84m10|t1 0.113 0.021 5.465 0.000 0.113 0.113 pe84m10|t2 1.258 0.026 48.321 0.000 1.258 1.258 pe85m10|t1 -0.487 0.022 -22.360 0.000 -0.487 -0.487 pe85m10|t2 0.857 0.022 38.311 0.000 0.857 0.857 pe96m10|t1 -0.034 0.020 -1.708 0.088 -0.034 -0.034 pe96m10|t2 1.074 0.024 43.859 0.000 1.074 1.074 pe97m10|t1 0.167 0.020 8.418 0.000 0.167 0.167 pe97m10|t2 1.139 0.024 47.739 0.000 1.139 1.139 pe92m10|t1 0.362 0.019 19.078 0.000 0.362 0.362 pe92m10|t2 1.149 0.023 50.961 0.000 1.149 1.149 pe93m10|t1 0.503 0.018 28.118 0.000 0.503 0.503 pe93m10|t2 1.226 0.023 53.261 0.000 1.226 1.226 pe94m10|t1 0.801 0.020 39.608 0.000 0.801 0.801 pe94m10|t2 1.543 0.031 50.095 0.000 1.543 1.543 pe95m10|t1 0.290 0.019 15.600 0.000 0.290 0.290 pe95m10|t2 0.943 0.021 44.002 0.000 0.943 0.943 pe64m10|t1 -0.072 0.020 -3.608 0.000 -0.072 -0.072 pe64m10|t2 0.871 0.023 38.544 0.000 0.871 0.871 pe84m12|t1 0.092 0.021 4.400 0.000 0.092 0.092 pe84m12|t2 1.296 0.027 48.842 0.000 1.296 1.296 pe85m12|t1 -0.297 0.021 -13.928 0.000 -0.297 -0.297 pe85m12|t2 0.944 0.023 41.137 0.000 0.944 0.944 pe96m12|t1 0.058 0.020 2.868 0.004 0.058 0.058 pe96m12|t2 1.119 0.024 46.929 0.000 1.119 1.119 pe97m12|t1 0.284 0.020 14.377 0.000 0.284 0.284 pe97m12|t2 1.198 0.024 50.731 0.000 1.198 1.198 pe92m12|t1 0.401 0.019 20.871 0.000 0.401 0.401 pe92m12|t2 1.244 0.024 51.829 0.000 1.244 1.244 pe93m12|t1 0.584 0.019 31.284 0.000 0.584 0.584 pe93m12|t2 1.284 0.023 55.594 0.000 1.284 1.284 pe94m12|t1 0.876 0.020 43.157 0.000 0.876 0.876 pe94m12|t2 1.576 0.031 51.129 0.000 1.576 1.576 pe95m12|t1 0.468 0.018 25.520 0.000 0.468 0.468 pe95m12|t2 1.082 0.023 47.423 0.000 1.082 1.082 pe64m12|t1 -0.044 0.020 -2.212 0.027 -0.044 -0.044 pe64m12|t2 0.874 0.023 38.832 0.000 0.874 0.874 pe2m5|t1 1.415 0.033 42.759 0.000 1.415 1.415 pe2m5|t2 2.417 0.074 32.641 0.000 2.417 2.417 pe4m5|t1 1.062 0.027 39.376 0.000 1.062 1.062 pe4m5|t2 2.184 0.057 38.495 0.000 2.184 2.184 pe7m5|t1 0.850 0.026 32.515 0.000 0.850 0.850 pe7m5|t2 1.974 0.050 39.758 0.000 1.974 1.974 pe11m5|t1 0.730 0.025 29.419 0.000 0.730 0.730 pe11m5|t2 1.825 0.042 43.723 0.000 1.825 1.825 pe13m5|t1 1.587 0.036 43.501 0.000 1.587 1.587 pe13m5|t2 2.659 0.099 26.975 0.000 2.659 2.659 pe25m5|t1 1.181 0.030 39.321 0.000 1.181 1.181 pe25m5|t2 2.292 0.066 34.916 0.000 2.292 2.292 pe2m7|t1 1.201 0.032 37.226 0.000 1.201 1.201 pe2m7|t2 2.284 0.059 38.715 0.000 2.284 2.284 pe4m7|t1 1.033 0.033 31.766 0.000 1.033 1.033 pe4m7|t2 2.280 0.053 42.634 0.000 2.280 2.280 pe7m7|t1 0.653 0.028 23.678 0.000 0.653 0.653 pe7m7|t2 1.822 0.042 43.439 0.000 1.822 1.822 pe11m7|t1 0.879 0.027 32.550 0.000 0.879 0.879 pe11m7|t2 1.910 0.042 45.762 0.000 1.910 1.910 pe13m7|t1 1.432 0.038 37.562 0.000 1.432 1.432 pe13m7|t2 2.580 0.080 32.218 0.000 2.580 2.580 pe25m7|t1 1.364 0.035 39.147 0.000 1.364 1.364 pe25m7|t2 2.302 0.060 38.146 0.000 2.302 2.302 pe2m10|t1 1.031 0.030 34.164 0.000 1.031 1.031 pe2m10|t2 2.160 0.054 40.358 0.000 2.160 2.160 pe4m10|t1 0.938 0.031 30.415 0.000 0.938 0.938 pe4m10|t2 2.168 0.046 46.951 0.000 2.168 2.168 pe7m10|t1 0.641 0.027 23.755 0.000 0.641 0.641 pe7m10|t2 1.931 0.045 42.640 0.000 1.931 1.931 pe11m10|t1 0.815 0.026 30.884 0.000 0.815 0.815 pe11m10|t2 2.000 0.046 43.409 0.000 2.000 2.000 pe13m10|t1 1.227 0.036 34.360 0.000 1.227 1.227 pe13m10|t2 2.365 0.060 39.095 0.000 2.365 2.365 pe25m10|t1 1.348 0.031 43.065 0.000 1.348 1.348 pe25m10|t2 2.313 0.063 36.866 0.000 2.313 2.313 pe2m12|t1 1.156 0.030 37.997 0.000 1.156 1.156 pe2m12|t2 2.264 0.058 38.772 0.000 2.264 2.264 pe4m12|t1 0.925 0.033 28.130 0.000 0.925 0.925 pe4m12|t2 2.246 0.051 44.440 0.000 2.246 2.246 pe7m12|t1 0.709 0.027 26.352 0.000 0.709 0.709 pe7m12|t2 1.998 0.049 41.104 0.000 1.998 1.998 pe11m12|t1 0.885 0.027 32.627 0.000 0.885 0.885 pe11m12|t2 2.019 0.047 43.003 0.000 2.019 2.019 pe13m12|t1 1.174 0.036 32.815 0.000 1.174 1.174 pe13m12|t2 2.259 0.055 41.425 0.000 2.259 2.259 pe25m12|t1 1.236 0.032 38.173 0.000 1.236 1.236 pe25m12|t2 2.438 0.073 33.188 0.000 2.438 2.438

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Fhyp5 0.000 0.000 0.000 .Fhyp7 0.000 0.000 0.000 .Fhyp10 0.000 0.000 0.000 .Fhyp12 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 .Fsi7 0.000 0.000 0.000 .Fsi10 0.000 0.000 0.000 .Fsi12 0.000 0.000 0.000 .pe84m5 0.513 0.513 0.513 .pe85m5 0.529 0.529 0.529 .pe96m5 0.616 0.616 0.616 .pe97m5 0.501 0.501 0.501 .pe92m5 0.422 0.422 0.422 .pe93m5 0.299 0.299 0.299 .pe94m5 0.392 0.392 0.392 .pe95m5 0.469 0.469 0.469 .pe64m5 0.647 0.647 0.647 .pe84m7 0.480 0.480 0.480 .pe85m7 0.497 0.497 0.497 .pe96m7 0.590 0.590 0.590 .pe97m7 0.468 0.468 0.468 .pe92m7 0.384 0.384 0.384 .pe93m7 0.252 0.252 0.252 .pe94m7 0.351 0.351 0.351 .pe95m7 0.433 0.433 0.433 .pe64m7 0.623 0.623 0.623 .pe84m10 0.436 0.436 0.436 .pe85m10 0.454 0.454 0.454 .pe96m10 0.555 0.555 0.555 .pe97m10 0.422 0.422 0.422 .pe92m10 0.331 0.331 0.331 .pe93m10 0.188 0.188 0.188 .pe94m10 0.295 0.295 0.295 .pe95m10 0.385 0.385 0.385 .pe64m10 0.591 0.591 0.591 .pe84m12 0.399 0.399 0.399 .pe85m12 0.418 0.418 0.418 .pe96m12 0.526 0.526 0.526 .pe97m12 0.384 0.384 0.384 .pe92m12 0.287 0.287 0.287 .pe93m12 0.135 0.135 0.135 .pe94m12 0.250 0.250 0.250 .pe95m12 0.345 0.345 0.345 .pe64m12 0.564 0.564 0.564 .pe2m5 0.635 0.635 0.635 .pe4m5 0.343 0.343 0.343 .pe7m5 0.635 0.635 0.635 .pe11m5 0.757 0.757 0.757 .pe13m5 0.256 0.256 0.256 .pe25m5 0.498 0.498 0.498 .pe2m7 0.580 0.580 0.580 .pe4m7 0.243 0.243 0.243 .pe7m7 0.579 0.579 0.579 .pe11m7 0.720 0.720 0.720 .pe13m7 0.143 0.143 0.143 .pe25m7 0.422 0.422 0.422 .pe2m10 0.585 0.585 0.585 .pe4m10 0.253 0.253 0.253 .pe7m10 0.585 0.585 0.585 .pe11m10 0.723 0.723 0.723 .pe13m10 0.155 0.155 0.155 .pe25m10 0.429 0.429 0.429 .pe2m12 0.561 0.561 0.561 .pe4m12 0.209 0.209 0.209 .pe7m12 0.561 0.561 0.561 .pe11m12 0.707 0.707 0.707 .pe13m12 0.105 0.105 0.105 .pe25m12 0.396 0.396 0.396 RIhyp 0.288 0.027 10.548 0.000 1.000 1.000 RIsi 0.057 0.095 0.603 0.547 1.000 1.000 WFhyp5 0.199 0.027 7.451 0.000 1.000 1.000 .WFhyp7 0.173 0.010 16.679 0.000 0.749 0.749 .WFhyp10 0.196 0.010 20.156 0.000 0.712 0.712 .WFhyp12 0.130 0.010 13.677 0.000 0.416 0.416 WFsi5 0.307 0.097 3.171 0.002 1.000 1.000 .WFsi7 0.162 0.019 8.676 0.000 0.448 0.448 .WFsi10 0.180 0.017 10.438 0.000 0.504 0.504 .WFsi12 0.102 0.014 7.417 0.000 0.268 0.268

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe84m5 1.000 1.000 1.000 pe85m5 1.000 1.000 1.000 pe96m5 1.000 1.000 1.000 pe97m5 1.000 1.000 1.000 pe92m5 1.000 1.000 1.000 pe93m5 1.000 1.000 1.000 pe94m5 1.000 1.000 1.000 pe95m5 1.000 1.000 1.000 pe64m5 1.000 1.000 1.000 pe84m7 1.000 1.000 1.000 pe85m7 1.000 1.000 1.000 pe96m7 1.000 1.000 1.000 pe97m7 1.000 1.000 1.000 pe92m7 1.000 1.000 1.000 pe93m7 1.000 1.000 1.000 pe94m7 1.000 1.000 1.000 pe95m7 1.000 1.000 1.000 pe64m7 1.000 1.000 1.000 pe84m10 1.000 1.000 1.000 pe85m10 1.000 1.000 1.000 pe96m10 1.000 1.000 1.000 pe97m10 1.000 1.000 1.000 pe92m10 1.000 1.000 1.000 pe93m10 1.000 1.000 1.000 pe94m10 1.000 1.000 1.000 pe95m10 1.000 1.000 1.000 pe64m10 1.000 1.000 1.000 pe84m12 1.000 1.000 1.000 pe85m12 1.000 1.000 1.000 pe96m12 1.000 1.000 1.000 pe97m12 1.000 1.000 1.000 pe92m12 1.000 1.000 1.000 pe93m12 1.000 1.000 1.000 pe94m12 1.000 1.000 1.000 pe95m12 1.000 1.000 1.000 pe64m12 1.000 1.000 1.000 pe2m5 1.000 1.000 1.000 pe4m5 1.000 1.000 1.000 pe7m5 1.000 1.000 1.000 pe11m5 1.000 1.000 1.000 pe13m5 1.000 1.000 1.000 pe25m5 1.000 1.000 1.000 pe2m7 1.000 1.000 1.000 pe4m7 1.000 1.000 1.000 pe7m7 1.000 1.000 1.000 pe11m7 1.000 1.000 1.000 pe13m7 1.000 1.000 1.000 pe25m7 1.000 1.000 1.000 pe2m10 1.000 1.000 1.000 pe4m10 1.000 1.000 1.000 pe7m10 1.000 1.000 1.000 pe11m10 1.000 1.000 1.000 pe13m10 1.000 1.000 1.000 pe25m10 1.000 1.000 1.000 pe2m12 1.000 1.000 1.000 pe4m12 1.000 1.000 1.000 pe7m12 1.000 1.000 1.000 pe11m12 1.000 1.000 1.000 pe13m12 1.000 1.000 1.000 pe25m12 1.000 1.000 1.000

S4 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S3 model) Comparative Fit Index (CFI) 0.907 (>0.95) Change in CFI: 0.079 (decrease) - worse fit Tucker-Lewis Index (TLI) 0.904 (>0.95) Change in TLI: 0.081 (decrease) - worse fit RMSEA 0.049 (≤ 0.06) Change in RMSEA: 0.029 (increase) - worse fit 90 Percent confidence interval - lower 0.058 90 Percent confidence interval - upper 0.040
SRMR 0.086 (≤ 0.08) Change in SRMR: 0.046 (increase) - worse fit

summary(semTools::compareFit(RICLPM_multi_hyp_S3.fit, RICLPM_multi_hyp_S4.fit, nested = TRUE)) #† indicates the best fitting model

Nested Model Comparison

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

                      Df AIC BIC   Chisq Chisq diff Df diff Pr(>Chisq)

RICLPM_multi_hyp_S3.fit 1592 3015.9
RICLPM_multi_hyp_S4.fit 1709 17780.1 3430.4 117 < 2.2e-16 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Model Fit Indices

                    chisq.scaled df.scaled pvalue.scaled rmsea.scaled

RICLPM_multi_hyp_S3.fit 2860.210† 1592 .000 .019† RICLPM_multi_hyp_S4.fit 10652.025 1709 .000 .048 cfi.scaled tli.scaled srmr RICLPM_multi_hyp_S3.fit .986† .985† .039† RICLPM_multi_hyp_S4.fit .905 .901 .086

Differences in Fit Indices

                                              df.scaled rmsea.scaled

RICLPM_multi_hyp_S4.fit - RICLPM_multi_hyp_S3.fit 117 0.03 cfi.scaled tli.scaled srmr RICLPM_multi_hyp_S4.fit - RICLPM_multi_hyp_S3.fit -0.082 -0.084 0.047

As all fit indices changed by more than 0.01 - we cannot accept this measurement model.

Multiple indicator RI-CLPM: ADHD and social isolation

RICLPM_multi_adhd_S1: ADHD step 1

Multiple response items RICLPM mother report total ADHD symptoms and social isolation: Step 1, the configural model (S1)

RICLPM_multi_adhd_S1 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of ADHD (mother report)
  # Inattention symptoms
  RIadhd1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIadhd2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIadhd3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIadhd4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIadhd5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIadhd6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIadhd7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIadhd8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIadhd9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  #Hyperactivity symptoms
  RIadhd10 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIadhd11 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIadhd12 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIadhd13 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIadhd14 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIadhd15 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIadhd16 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIadhd17 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIadhd18 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for ADHD (inattention and hyperactivity) symptoms at 4 waves
  WFadhd5 =~ pe81m5 + pe82m5 + pe83m5 + pe86m5 + pe87m5 + pe88m5 + pe89m5 + pe90m5 + pe91m5 + pe84m5 + pe85m5 + pe96m5 + pe97m5 + pe92m5 + pe93m5 + pe94m5 + pe95m5 + pe64m5
  WFadhd7 =~ pe81m7 + pe82m7 + pe83m7 + pe86m7 + pe87m7 + pe88m7 + pe89m7 + pe90m7 + pe91m7 + pe84m7 + pe85m7 + pe96m7 + pe97m7 + pe92m7 + pe93m7 + pe94m7 + pe95m7 + pe64m7
  WFadhd10 =~ pe81m10 + pe82m10 + pe83m10 + pe86m10 + pe87m10 + pe88m10 + pe89m10 + pe90m10 + pe91m10 + pe84m10 + pe85m10 + pe96m10 + pe97m10 + pe92m10 + pe93m10 + pe94m10 + pe95m10 + pe64m10
  WFadhd12 =~ pe81m12 + pe82m12 + pe83m12 + pe86m12 + pe87m12 + pe88m12 + pe89m12 + pe90m12 + pe91m12 + pe84m12 + pe85m12 + pe96m12 + pe97m12 + pe92m12 + pe93m12 + pe94m12 + pe95m12 + pe64m12 
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ pe2m5 + pe4m5 + pe7m5 + pe11m5 + pe13m5 + pe25m5 
  WFsi7 =~ pe2m7 + pe4m7 + pe7m7 + pe11m7 + pe13m7 + pe25m7 
  WFsi10 =~ pe2m10 + pe4m10 + pe7m10 + pe11m10 + pe13m10 + pe25m10
  WFsi12 =~ pe2m12 + pe4m12 + pe7m12 + pe11m12 + pe13m12 + pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFadhd7 + WFsi7 ~ WFadhd5 + WFsi5
  WFadhd10 + WFsi10 ~ WFadhd7 + WFsi7
  WFadhd12 + WFsi12 ~ WFadhd10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFadhd5 ~~ WFsi5
  WFadhd7 ~~ WFsi7
  WFadhd10 ~~ WFsi10 
  WFadhd12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIadhd1 + RIadhd2 + RIadhd3 + RIadhd4 + RIadhd5 + RIadhd6 + RIadhd7 + RIadhd8 + RIadhd9 + RIadhd10 + RIadhd11 + RIadhd12 + RIadhd13 + RIadhd14 + RIadhd15 + RIadhd16 + RIadhd17 + RIadhd18 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFadhd5
'

RICLPM_multi_adhd_S1.fit <- cfa(RICLPM_multi_adhd_S1, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,        
                           missing = 'pairwise'  
)

summary(RICLPM_multi_adhd_S1.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 228 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 604

Number of observations 2232 Number of missing patterns 70

Model Test User Model: Standard Robust Test Statistic 5796.868 6406.447 Degrees of freedom 4148 4148 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.502 Shift parameter 2546.163 simple second-order correction

Model Test Baseline Model:

Test statistic 953296.763 166852.672 Degrees of freedom 4560 4560 P-value 0.000 0.000 Scaling correction factor 5.846

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.998 0.986 Tucker-Lewis Index (TLI) 0.998 0.985

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.013 0.016 90 Percent confidence interval - lower 0.013 0.015 90 Percent confidence interval - upper 0.014 0.016 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.032 0.032

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIadhd1 =~
pe81m5 1.000 0.599 0.599 pe81m7 1.000 0.599 0.599 pe81m10 1.000 0.599 0.599 pe81m12 1.000 0.599 0.599 RIadhd2 =~
pe82m5 1.000 0.515 0.515 pe82m7 1.000 0.515 0.515 pe82m10 1.000 0.515 0.515 pe82m12 1.000 0.515 0.515 RIadhd3 =~
pe83m5 1.000 0.534 0.534 pe83m7 1.000 0.534 0.534 pe83m10 1.000 0.534 0.534 pe83m12 1.000 0.534 0.534 RIadhd4 =~
pe86m5 1.000 0.562 0.562 pe86m7 1.000 0.562 0.562 pe86m10 1.000 0.562 0.562 pe86m12 1.000 0.562 0.562 RIadhd5 =~
pe87m5 1.000 0.613 0.613 pe87m7 1.000 0.613 0.613 pe87m10 1.000 0.613 0.613 pe87m12 1.000 0.613 0.613 RIadhd6 =~
pe88m5 1.000 0.539 0.539 pe88m7 1.000 0.539 0.539 pe88m10 1.000 0.539 0.539 pe88m12 1.000 0.539 0.539 RIadhd7 =~
pe89m5 1.000 0.578 0.578 pe89m7 1.000 0.578 0.578 pe89m10 1.000 0.578 0.578 pe89m12 1.000 0.578 0.578 RIadhd8 =~
pe90m5 1.000 0.587 0.587 pe90m7 1.000 0.587 0.587 pe90m10 1.000 0.587 0.587 pe90m12 1.000 0.587 0.587 RIadhd9 =~
pe91m5 1.000 0.609 0.609 pe91m7 1.000 0.609 0.609 pe91m10 1.000 0.609 0.609 pe91m12 1.000 0.609 0.609 RIadhd10 =~
pe84m5 1.000 0.614 0.614 pe84m7 1.000 0.614 0.614 pe84m10 1.000 0.614 0.614 pe84m12 1.000 0.614 0.614 RIadhd11 =~
pe85m5 1.000 0.599 0.599 pe85m7 1.000 0.599 0.599 pe85m10 1.000 0.599 0.599 pe85m12 1.000 0.599 0.599 RIadhd12 =~
pe96m5 1.000 0.625 0.625 pe96m7 1.000 0.625 0.625 pe96m10 1.000 0.625 0.625 pe96m12 1.000 0.625 0.625 RIadhd13 =~
pe97m5 1.000 0.598 0.598 pe97m7 1.000 0.598 0.598 pe97m10 1.000 0.598 0.598 pe97m12 1.000 0.598 0.598 RIadhd14 =~
pe92m5 1.000 0.634 0.634 pe92m7 1.000 0.634 0.634 pe92m10 1.000 0.634 0.634 pe92m12 1.000 0.634 0.634 RIadhd15 =~
pe93m5 1.000 0.632 0.632 pe93m7 1.000 0.632 0.632 pe93m10 1.000 0.632 0.632 pe93m12 1.000 0.632 0.632 RIadhd16 =~
pe94m5 1.000 0.602 0.602 pe94m7 1.000 0.602 0.602 pe94m10 1.000 0.602 0.602 pe94m12 1.000 0.602 0.602 RIadhd17 =~
pe95m5 1.000 0.695 0.695 pe95m7 1.000 0.695 0.695 pe95m10 1.000 0.695 0.695 pe95m12 1.000 0.695 0.695 RIadhd18 =~
pe64m5 1.000 0.760 0.760 pe64m7 1.000 0.760 0.760 pe64m10 1.000 0.760 0.760 pe64m12 1.000 0.760 0.760 RIsi1 =~
pe2m5 1.000 0.431 0.431 pe2m7 1.000 0.431 0.431 pe2m10 1.000 0.431 0.431 pe2m12 1.000 0.431 0.431 RIsi2 =~
pe4m5 1.000 0.078 0.078 pe4m7 1.000 0.078 0.078 pe4m10 1.000 0.078 0.078 pe4m12 1.000 0.078 0.078 RIsi3 =~
pe7m5 1.000 0.443 0.443 pe7m7 1.000 0.443 0.443 pe7m10 1.000 0.443 0.443 pe7m12 1.000 0.443 0.443 RIsi4 =~
pe11m5 1.000 0.627 0.627 pe11m7 1.000 0.627 0.627 pe11m10 1.000 0.627 0.627 pe11m12 1.000 0.627 0.627 RIsi5 =~
pe13m5 1.000 0.359 0.359 pe13m7 1.000 0.359 0.359 pe13m10 1.000 0.359 0.359 pe13m12 1.000 0.359 0.359 RIsi6 =~
pe25m5 1.000 0.385 0.385 pe25m7 1.000 0.385 0.385 pe25m10 1.000 0.385 0.385 pe25m12 1.000 0.385 0.385 WFadhd5 =~
pe81m5 1.000 0.642 0.642 pe82m5 1.007 0.039 25.889 0.000 0.646 0.646 pe83m5 1.067 0.036 29.282 0.000 0.685 0.685 pe86m5 0.916 0.043 21.519 0.000 0.588 0.588 pe87m5 0.631 0.049 12.838 0.000 0.405 0.405 pe88m5 0.867 0.052 16.613 0.000 0.556 0.556 pe89m5 0.876 0.049 17.971 0.000 0.562 0.562 pe90m5 0.804 0.049 16.312 0.000 0.516 0.516 pe91m5 0.787 0.050 15.755 0.000 0.505 0.505 pe84m5 0.758 0.042 17.998 0.000 0.487 0.487 pe85m5 0.737 0.046 16.043 0.000 0.473 0.473 pe96m5 0.355 0.060 5.946 0.000 0.228 0.228 pe97m5 0.755 0.047 16.155 0.000 0.485 0.485 pe92m5 0.887 0.042 21.022 0.000 0.570 0.570 pe93m5 1.009 0.043 23.548 0.000 0.648 0.648 pe94m5 1.019 0.044 23.321 0.000 0.654 0.654 pe95m5 0.735 0.048 15.425 0.000 0.472 0.472 pe64m5 0.389 0.044 8.753 0.000 0.250 0.250 WFadhd7 =~
pe81m7 1.000 0.669 0.669 pe82m7 0.994 0.038 26.205 0.000 0.665 0.665 pe83m7 1.031 0.035 29.673 0.000 0.689 0.689 pe86m7 0.920 0.041 22.523 0.000 0.615 0.615 pe87m7 0.675 0.047 14.406 0.000 0.452 0.452 pe88m7 0.824 0.047 17.369 0.000 0.551 0.551 pe89m7 0.803 0.045 17.694 0.000 0.537 0.537 pe90m7 0.796 0.048 16.457 0.000 0.532 0.532 pe91m7 0.924 0.047 19.503 0.000 0.618 0.618 pe84m7 0.816 0.040 20.331 0.000 0.546 0.546 pe85m7 0.759 0.043 17.832 0.000 0.508 0.508 pe96m7 0.542 0.047 11.485 0.000 0.362 0.362 pe97m7 0.748 0.044 16.809 0.000 0.500 0.500 pe92m7 0.902 0.040 22.309 0.000 0.603 0.603 pe93m7 0.983 0.040 24.659 0.000 0.657 0.657 pe94m7 1.028 0.042 24.321 0.000 0.687 0.687 pe95m7 0.767 0.046 16.666 0.000 0.513 0.513 pe64m7 0.292 0.046 6.377 0.000 0.196 0.196 WFadhd10 =~
pe81m10 1.000 0.681 0.681 pe82m10 1.000 0.034 29.421 0.000 0.681 0.681 pe83m10 1.052 0.030 34.982 0.000 0.717 0.717 pe86m10 0.873 0.038 23.272 0.000 0.595 0.595 pe87m10 0.732 0.042 17.508 0.000 0.499 0.499 pe88m10 0.799 0.042 19.025 0.000 0.545 0.545 pe89m10 0.788 0.043 18.238 0.000 0.537 0.537 pe90m10 0.689 0.045 15.309 0.000 0.470 0.470 pe91m10 0.807 0.044 18.206 0.000 0.550 0.550 pe84m10 0.872 0.038 23.036 0.000 0.594 0.594 pe85m10 0.857 0.040 21.650 0.000 0.584 0.584 pe96m10 0.751 0.045 16.870 0.000 0.512 0.512 pe97m10 0.834 0.040 21.075 0.000 0.568 0.568 pe92m10 0.945 0.038 25.103 0.000 0.644 0.644 pe93m10 0.954 0.035 27.435 0.000 0.650 0.650 pe94m10 0.963 0.040 23.884 0.000 0.657 0.657 pe95m10 0.646 0.041 15.808 0.000 0.440 0.440 pe64m10 0.478 0.042 11.285 0.000 0.325 0.325 WFadhd12 =~
pe81m12 1.000 0.726 0.726 pe82m12 1.020 0.029 35.155 0.000 0.741 0.741 pe83m12 1.008 0.025 39.645 0.000 0.732 0.732 pe86m12 0.903 0.031 29.015 0.000 0.655 0.655 pe87m12 0.787 0.038 20.452 0.000 0.571 0.571 pe88m12 0.788 0.037 21.208 0.000 0.572 0.572 pe89m12 0.761 0.037 20.328 0.000 0.552 0.552 pe90m12 0.723 0.038 18.927 0.000 0.525 0.525 pe91m12 0.824 0.039 21.255 0.000 0.598 0.598 pe84m12 0.855 0.032 26.824 0.000 0.620 0.620 pe85m12 0.822 0.033 24.753 0.000 0.597 0.597 pe96m12 0.840 0.043 19.733 0.000 0.610 0.610 pe97m12 0.854 0.034 25.160 0.000 0.620 0.620 pe92m12 0.875 0.032 27.781 0.000 0.635 0.635 pe93m12 0.919 0.031 30.121 0.000 0.667 0.667 pe94m12 0.954 0.034 28.213 0.000 0.692 0.692 pe95m12 0.644 0.036 18.114 0.000 0.467 0.467 pe64m12 0.501 0.038 13.237 0.000 0.364 0.364 WFsi5 =~
pe2m5 1.000 0.571 0.571 pe4m5 1.529 0.177 8.642 0.000 0.873 0.873 pe7m5 0.870 0.113 7.675 0.000 0.497 0.497 pe11m5 0.877 0.121 7.271 0.000 0.501 0.501 pe13m5 1.396 0.173 8.072 0.000 0.797 0.797 pe25m5 1.316 0.153 8.620 0.000 0.751 0.751 WFsi7 =~
pe2m7 1.000 0.567 0.567 pe4m7 1.573 0.168 9.342 0.000 0.892 0.892 pe7m7 1.061 0.123 8.639 0.000 0.601 0.601 pe11m7 0.880 0.115 7.661 0.000 0.499 0.499 pe13m7 1.512 0.172 8.800 0.000 0.857 0.857 pe25m7 1.359 0.155 8.777 0.000 0.771 0.771 WFsi10 =~
pe2m10 1.000 0.621 0.621 pe4m10 1.361 0.121 11.216 0.000 0.845 0.845 pe7m10 1.022 0.102 10.041 0.000 0.634 0.634 pe11m10 0.714 0.091 7.854 0.000 0.443 0.443 pe13m10 1.361 0.129 10.567 0.000 0.845 0.845 pe25m10 1.234 0.123 10.054 0.000 0.766 0.766 WFsi12 =~
pe2m12 1.000 0.707 0.707 pe4m12 1.252 0.091 13.715 0.000 0.885 0.885 pe7m12 1.034 0.081 12.831 0.000 0.731 0.731 pe11m12 0.790 0.075 10.483 0.000 0.559 0.559 pe13m12 1.210 0.096 12.632 0.000 0.856 0.856 pe25m12 1.127 0.093 12.141 0.000 0.797 0.797

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd7 ~
WFadhd5 0.477 0.064 7.449 0.000 0.458 0.458 WFsi5 0.280 0.076 3.664 0.000 0.239 0.239 WFsi7 ~
WFadhd5 0.183 0.065 2.831 0.005 0.207 0.207 WFsi5 0.613 0.106 5.794 0.000 0.617 0.617 WFadhd10 ~
WFadhd7 0.420 0.055 7.609 0.000 0.412 0.412 WFsi7 0.366 0.081 4.529 0.000 0.305 0.305 WFsi10 ~
WFadhd7 0.271 0.056 4.796 0.000 0.292 0.292 WFsi7 0.624 0.084 7.417 0.000 0.570 0.570 WFadhd12 ~
WFadhd10 0.688 0.040 17.010 0.000 0.646 0.646 WFsi10 0.235 0.056 4.161 0.000 0.201 0.201 WFsi12 ~
WFadhd10 0.130 0.047 2.764 0.006 0.125 0.125 WFsi10 0.910 0.074 12.233 0.000 0.800 0.800

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd5 ~~
WFsi5 0.240 0.055 4.375 0.000 0.656 0.656 .WFadhd7 ~~
.WFsi7 0.066 0.013 5.078 0.000 0.352 0.352 .WFadhd10 ~~
.WFsi10 0.058 0.012 4.845 0.000 0.294 0.294 .WFadhd12 ~~
.WFsi12 0.048 0.011 4.531 0.000 0.328 0.328 RIadhd1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd7 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd8 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd9 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd10 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd11 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd12 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd13 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd14 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd15 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd16 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd17 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd18 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd1 ~~
RIadhd2 0.297 0.053 5.628 0.000 0.963 0.963 RIadhd3 0.335 0.054 6.254 0.000 1.048 1.048 RIadhd4 0.245 0.047 5.209 0.000 0.726 0.726 RIadhd5 0.145 0.039 3.743 0.000 0.395 0.395 RIadhd6 0.146 0.043 3.379 0.001 0.454 0.454 RIadhd7 0.262 0.044 6.015 0.000 0.757 0.757 RIadhd8 0.051 0.041 1.241 0.215 0.145 0.145 RIadhd9 0.196 0.046 4.291 0.000 0.537 0.537 RIadhd10 0.212 0.044 4.833 0.000 0.575 0.575 RIadhd11 0.117 0.042 2.804 0.005 0.327 0.327 RIadhd12 0.014 0.036 0.402 0.688 0.038 0.038 RIadhd13 0.110 0.042 2.637 0.008 0.307 0.307 RIadhd14 0.210 0.048 4.384 0.000 0.552 0.552 RIadhd15 0.254 0.051 5.020 0.000 0.670 0.670 RIadhd16 0.288 0.052 5.535 0.000 0.800 0.800 RIadhd17 0.191 0.039 4.941 0.000 0.460 0.460 RIadhd18 0.104 0.028 3.710 0.000 0.229 0.229 RIsi1 -0.049 0.053 -0.911 0.362 -0.189 -0.189 RIsi2 -0.036 0.067 -0.535 0.592 -0.770 -0.770 RIsi3 -0.053 0.052 -1.021 0.307 -0.198 -0.198 RIsi4 -0.059 0.043 -1.373 0.170 -0.158 -0.158 RIsi5 0.010 0.066 0.154 0.878 0.047 0.047 RIsi6 -0.002 0.062 -0.040 0.968 -0.011 -0.011 RIadhd2 ~~
RIadhd3 0.254 0.054 4.657 0.000 0.923 0.923 RIadhd4 0.226 0.048 4.731 0.000 0.782 0.782 RIadhd5 0.108 0.039 2.732 0.006 0.341 0.341 RIadhd6 0.148 0.044 3.378 0.001 0.534 0.534 RIadhd7 0.241 0.044 5.438 0.000 0.809 0.809 RIadhd8 0.071 0.041 1.714 0.087 0.235 0.235 RIadhd9 0.168 0.046 3.636 0.000 0.536 0.536 RIadhd10 0.173 0.044 3.909 0.000 0.548 0.548 RIadhd11 0.082 0.042 1.942 0.052 0.264 0.264 RIadhd12 -0.009 0.035 -0.245 0.806 -0.027 -0.027 RIadhd13 0.070 0.042 1.677 0.093 0.228 0.228 RIadhd14 0.124 0.048 2.563 0.010 0.379 0.379 RIadhd15 0.146 0.051 2.853 0.004 0.448 0.448 RIadhd16 0.209 0.053 3.973 0.000 0.676 0.676 RIadhd17 0.098 0.039 2.528 0.011 0.275 0.275 RIadhd18 0.047 0.028 1.699 0.089 0.120 0.120 RIsi1 -0.079 0.053 -1.483 0.138 -0.358 -0.358 RIsi2 -0.076 0.067 -1.126 0.260 -1.900 -1.900 RIsi3 -0.072 0.052 -1.390 0.164 -0.317 -0.317 RIsi4 -0.077 0.044 -1.779 0.075 -0.240 -0.240 RIsi5 -0.044 0.066 -0.661 0.509 -0.237 -0.237 RIsi6 -0.041 0.062 -0.654 0.513 -0.206 -0.206 RIadhd3 ~~
RIadhd4 0.220 0.049 4.525 0.000 0.733 0.733 RIadhd5 0.126 0.040 3.142 0.002 0.384 0.384 RIadhd6 0.126 0.045 2.841 0.004 0.439 0.439 RIadhd7 0.213 0.045 4.732 0.000 0.689 0.689 RIadhd8 0.054 0.042 1.279 0.201 0.173 0.173 RIadhd9 0.174 0.047 3.706 0.000 0.534 0.534 RIadhd10 0.231 0.045 5.117 0.000 0.705 0.705 RIadhd11 0.133 0.043 3.067 0.002 0.414 0.414 RIadhd12 0.050 0.037 1.365 0.172 0.150 0.150 RIadhd13 0.118 0.043 2.768 0.006 0.370 0.370 RIadhd14 0.185 0.049 3.766 0.000 0.548 0.548 RIadhd15 0.216 0.052 4.139 0.000 0.639 0.639 RIadhd16 0.253 0.054 4.720 0.000 0.788 0.788 RIadhd17 0.194 0.040 4.888 0.000 0.521 0.521 RIadhd18 0.139 0.028 4.982 0.000 0.343 0.343 RIsi1 -0.072 0.055 -1.309 0.191 -0.314 -0.314 RIsi2 -0.080 0.069 -1.159 0.246 -1.924 -1.924 RIsi3 -0.068 0.053 -1.271 0.204 -0.286 -0.286 RIsi4 -0.092 0.044 -2.079 0.038 -0.276 -0.276 RIsi5 -0.030 0.069 -0.433 0.665 -0.155 -0.155 RIsi6 -0.041 0.064 -0.641 0.521 -0.198 -0.198 RIadhd4 ~~
RIadhd5 0.170 0.036 4.789 0.000 0.495 0.495 RIadhd6 0.193 0.040 4.860 0.000 0.637 0.637 RIadhd7 0.254 0.040 6.372 0.000 0.780 0.780 RIadhd8 0.111 0.038 2.941 0.003 0.336 0.336 RIadhd9 0.193 0.042 4.655 0.000 0.565 0.565 RIadhd10 0.236 0.040 5.850 0.000 0.682 0.682 RIadhd11 0.166 0.038 4.320 0.000 0.494 0.494 RIadhd12 0.068 0.033 2.063 0.039 0.192 0.192 RIadhd13 0.129 0.038 3.358 0.001 0.383 0.383 RIadhd14 0.157 0.044 3.577 0.000 0.440 0.440 RIadhd15 0.179 0.046 3.898 0.000 0.505 0.505 RIadhd16 0.207 0.047 4.406 0.000 0.613 0.613 RIadhd17 0.131 0.035 3.700 0.000 0.336 0.336 RIadhd18 0.107 0.026 4.119 0.000 0.250 0.250 RIsi1 -0.062 0.048 -1.306 0.191 -0.257 -0.257 RIsi2 -0.030 0.060 -0.495 0.621 -0.679 -0.679 RIsi3 -0.034 0.047 -0.726 0.468 -0.136 -0.136 RIsi4 -0.075 0.039 -1.914 0.056 -0.212 -0.212 RIsi5 0.014 0.060 0.242 0.809 0.071 0.071 RIsi6 -0.020 0.055 -0.363 0.716 -0.093 -0.093 RIadhd5 ~~
RIadhd6 0.185 0.033 5.546 0.000 0.560 0.560 RIadhd7 0.154 0.033 4.636 0.000 0.435 0.435 RIadhd8 0.118 0.032 3.678 0.000 0.327 0.327 RIadhd9 0.238 0.035 6.758 0.000 0.637 0.637 RIadhd10 0.177 0.033 5.294 0.000 0.470 0.470 RIadhd11 0.149 0.032 4.649 0.000 0.406 0.406 RIadhd12 0.067 0.027 2.436 0.015 0.174 0.174 RIadhd13 0.116 0.032 3.657 0.000 0.315 0.315 RIadhd14 0.131 0.037 3.579 0.000 0.337 0.337 RIadhd15 0.135 0.038 3.524 0.000 0.348 0.348 RIadhd16 0.137 0.039 3.497 0.000 0.372 0.372 RIadhd17 0.098 0.030 3.220 0.001 0.230 0.230 RIadhd18 0.082 0.023 3.595 0.000 0.175 0.175 RIsi1 0.040 0.039 1.025 0.305 0.153 0.153 RIsi2 0.054 0.049 1.110 0.267 1.141 1.141 RIsi3 -0.008 0.038 -0.214 0.831 -0.030 -0.030 RIsi4 0.084 0.033 2.554 0.011 0.220 0.220 RIsi5 0.102 0.049 2.076 0.038 0.466 0.466 RIsi6 0.096 0.045 2.143 0.032 0.407 0.407 RIadhd6 ~~
RIadhd7 0.183 0.037 4.944 0.000 0.586 0.586 RIadhd8 0.248 0.035 7.027 0.000 0.784 0.784 RIadhd9 0.219 0.039 5.652 0.000 0.668 0.668 RIadhd10 0.167 0.037 4.482 0.000 0.504 0.504 RIadhd11 0.095 0.036 2.668 0.008 0.295 0.295 RIadhd12 0.016 0.030 0.544 0.586 0.049 0.049 RIadhd13 0.065 0.035 1.836 0.066 0.202 0.202 RIadhd14 0.098 0.040 2.422 0.015 0.286 0.286 RIadhd15 0.096 0.042 2.271 0.023 0.282 0.282 RIadhd16 0.110 0.044 2.514 0.012 0.338 0.338 RIadhd17 0.049 0.033 1.500 0.134 0.130 0.130 RIadhd18 0.057 0.024 2.362 0.018 0.138 0.138 RIsi1 -0.071 0.045 -1.571 0.116 -0.308 -0.308 RIsi2 -0.023 0.056 -0.405 0.686 -0.540 -0.540 RIsi3 -0.050 0.044 -1.142 0.253 -0.210 -0.210 RIsi4 -0.049 0.036 -1.348 0.178 -0.145 -0.145 RIsi5 0.011 0.056 0.192 0.848 0.056 0.056 RIsi6 -0.019 0.052 -0.374 0.708 -0.093 -0.093 RIadhd7 ~~
RIadhd8 0.083 0.035 2.388 0.017 0.244 0.244 RIadhd9 0.183 0.039 4.713 0.000 0.520 0.520 RIadhd10 0.156 0.037 4.213 0.000 0.439 0.439 RIadhd11 0.090 0.035 2.550 0.011 0.260 0.260 RIadhd12 -0.015 0.030 -0.506 0.613 -0.042 -0.042 RIadhd13 0.079 0.035 2.244 0.025 0.228 0.228 RIadhd14 0.136 0.041 3.343 0.001 0.370 0.370 RIadhd15 0.149 0.042 3.504 0.000 0.407 0.407 RIadhd16 0.193 0.044 4.384 0.000 0.553 0.553 RIadhd17 0.065 0.033 1.968 0.049 0.161 0.161 RIadhd18 0.012 0.025 0.490 0.624 0.027 0.027 RIsi1 -0.001 0.045 -0.033 0.974 -0.006 -0.006 RIsi2 0.011 0.056 0.190 0.849 0.237 0.237 RIsi3 -0.008 0.043 -0.185 0.853 -0.031 -0.031 RIsi4 0.009 0.037 0.239 0.811 0.025 0.025 RIsi5 0.045 0.056 0.814 0.416 0.219 0.219 RIsi6 0.082 0.053 1.569 0.117 0.370 0.370 RIadhd8 ~~
RIadhd9 0.227 0.037 6.093 0.000 0.636 0.636 RIadhd10 0.147 0.035 4.134 0.000 0.407 0.407 RIadhd11 0.122 0.034 3.594 0.000 0.346 0.346 RIadhd12 0.061 0.029 2.125 0.034 0.167 0.167 RIadhd13 0.064 0.034 1.918 0.055 0.183 0.183 RIadhd14 0.077 0.038 2.016 0.044 0.207 0.207 RIadhd15 0.050 0.040 1.249 0.212 0.135 0.135 RIadhd16 0.044 0.041 1.062 0.288 0.123 0.123 RIadhd17 0.029 0.031 0.946 0.344 0.072 0.072 RIadhd18 0.089 0.023 3.861 0.000 0.200 0.200 RIsi1 -0.025 0.043 -0.590 0.555 -0.100 -0.100 RIsi2 -0.018 0.053 -0.345 0.730 -0.403 -0.403 RIsi3 -0.034 0.041 -0.822 0.411 -0.131 -0.131 RIsi4 -0.055 0.035 -1.566 0.117 -0.149 -0.149 RIsi5 0.005 0.054 0.085 0.932 0.022 0.022 RIsi6 -0.077 0.049 -1.564 0.118 -0.341 -0.341 RIadhd9 ~~
RIadhd10 0.198 0.039 5.110 0.000 0.530 0.530 RIadhd11 0.114 0.037 3.050 0.002 0.311 0.311 RIadhd12 0.064 0.032 1.979 0.048 0.167 0.167 RIadhd13 0.084 0.037 2.247 0.025 0.230 0.230 RIadhd14 0.156 0.043 3.659 0.000 0.404 0.404 RIadhd15 0.140 0.045 3.124 0.002 0.363 0.363 RIadhd16 0.181 0.046 3.944 0.000 0.494 0.494 RIadhd17 0.107 0.035 3.048 0.002 0.252 0.252 RIadhd18 0.095 0.026 3.641 0.000 0.206 0.206 RIsi1 0.007 0.048 0.154 0.878 0.028 0.028 RIsi2 -0.013 0.059 -0.219 0.827 -0.271 -0.271 RIsi3 -0.026 0.046 -0.564 0.573 -0.096 -0.096 RIsi4 0.013 0.038 0.329 0.742 0.033 0.033 RIsi5 0.023 0.060 0.379 0.705 0.104 0.104 RIsi6 0.052 0.054 0.954 0.340 0.220 0.220 RIadhd10 ~~
RIadhd11 0.249 0.036 6.909 0.000 0.678 0.678 RIadhd12 0.177 0.031 5.762 0.000 0.462 0.462 RIadhd13 0.211 0.036 5.914 0.000 0.574 0.574 RIadhd14 0.224 0.041 5.494 0.000 0.576 0.576 RIadhd15 0.236 0.043 5.509 0.000 0.608 0.608 RIadhd16 0.213 0.044 4.839 0.000 0.577 0.577 RIadhd17 0.256 0.033 7.700 0.000 0.599 0.599 RIadhd18 0.211 0.024 8.673 0.000 0.452 0.452 RIsi1 -0.014 0.045 -0.316 0.752 -0.053 -0.053 RIsi2 0.044 0.055 0.786 0.432 0.913 0.913 RIsi3 0.054 0.044 1.244 0.213 0.200 0.200 RIsi4 -0.103 0.036 -2.878 0.004 -0.267 -0.267 RIsi5 0.103 0.055 1.872 0.061 0.467 0.467 RIsi6 -0.023 0.051 -0.444 0.657 -0.095 -0.095 RIadhd11 ~~
RIadhd12 0.245 0.029 8.320 0.000 0.654 0.654 RIadhd13 0.279 0.034 8.172 0.000 0.778 0.778 RIadhd14 0.163 0.039 4.132 0.000 0.429 0.429 RIadhd15 0.186 0.041 4.509 0.000 0.492 0.492 RIadhd16 0.154 0.042 3.658 0.000 0.428 0.428 RIadhd17 0.224 0.032 7.067 0.000 0.537 0.537 RIadhd18 0.302 0.023 13.318 0.000 0.662 0.662 RIsi1 -0.009 0.043 -0.202 0.840 -0.033 -0.033 RIsi2 -0.002 0.053 -0.044 0.965 -0.050 -0.050 RIsi3 0.049 0.042 1.171 0.242 0.184 0.184 RIsi4 -0.096 0.035 -2.755 0.006 -0.257 -0.257 RIsi5 0.076 0.053 1.425 0.154 0.353 0.353 RIsi6 -0.059 0.049 -1.205 0.228 -0.257 -0.257 RIadhd12 ~~
RIadhd13 0.325 0.029 11.200 0.000 0.870 0.870 RIadhd14 0.116 0.034 3.446 0.001 0.294 0.294 RIadhd15 0.143 0.035 4.061 0.000 0.363 0.363 RIadhd16 0.106 0.036 2.969 0.003 0.283 0.283 RIadhd17 0.264 0.027 9.674 0.000 0.607 0.607 RIadhd18 0.321 0.020 16.127 0.000 0.675 0.675 RIsi1 -0.003 0.037 -0.095 0.925 -0.013 -0.013 RIsi2 -0.021 0.046 -0.452 0.651 -0.425 -0.425 RIsi3 0.038 0.035 1.079 0.281 0.137 0.137 RIsi4 -0.083 0.031 -2.709 0.007 -0.212 -0.212 RIsi5 0.029 0.047 0.626 0.532 0.131 0.131 RIsi6 -0.112 0.042 -2.664 0.008 -0.466 -0.466 RIadhd13 ~~
RIadhd14 0.155 0.039 3.984 0.000 0.409 0.409 RIadhd15 0.204 0.041 4.971 0.000 0.539 0.539 RIadhd16 0.197 0.042 4.661 0.000 0.547 0.547 RIadhd17 0.262 0.031 8.329 0.000 0.629 0.629 RIadhd18 0.256 0.022 11.449 0.000 0.564 0.564 RIsi1 -0.005 0.043 -0.124 0.901 -0.021 -0.021 RIsi2 0.041 0.053 0.773 0.440 0.885 0.885 RIsi3 0.062 0.041 1.514 0.130 0.234 0.234 RIsi4 -0.093 0.035 -2.678 0.007 -0.248 -0.248 RIsi5 0.079 0.054 1.482 0.138 0.369 0.369 RIsi6 -0.055 0.049 -1.127 0.260 -0.237 -0.237 RIadhd14 ~~
RIadhd15 0.393 0.047 8.338 0.000 0.981 0.981 RIadhd16 0.273 0.048 5.657 0.000 0.716 0.716 RIadhd17 0.288 0.036 8.019 0.000 0.654 0.654 RIadhd18 0.181 0.026 6.850 0.000 0.376 0.376 RIsi1 -0.037 0.050 -0.740 0.459 -0.135 -0.135 RIsi2 -0.028 0.062 -0.453 0.651 -0.570 -0.570 RIsi3 -0.028 0.047 -0.597 0.551 -0.100 -0.100 RIsi4 -0.077 0.040 -1.935 0.053 -0.195 -0.195 RIsi5 0.006 0.062 0.096 0.924 0.026 0.026 RIsi6 -0.036 0.057 -0.636 0.525 -0.147 -0.147 RIadhd15 ~~
RIadhd16 0.360 0.051 7.014 0.000 0.947 0.947 RIadhd17 0.367 0.038 9.720 0.000 0.835 0.835 RIadhd18 0.211 0.027 7.668 0.000 0.439 0.439 RIsi1 -0.053 0.052 -1.021 0.307 -0.196 -0.196 RIsi2 -0.015 0.066 -0.234 0.815 -0.312 -0.312 RIsi3 -0.038 0.050 -0.757 0.449 -0.135 -0.135 RIsi4 -0.078 0.042 -1.874 0.061 -0.198 -0.198 RIsi5 0.019 0.065 0.283 0.777 0.082 0.082 RIsi6 -0.037 0.060 -0.621 0.535 -0.153 -0.153 RIadhd16 ~~
RIadhd17 0.326 0.039 8.352 0.000 0.780 0.780 RIadhd18 0.171 0.028 6.074 0.000 0.374 0.374 RIsi1 -0.030 0.054 -0.546 0.585 -0.114 -0.114 RIsi2 -0.042 0.067 -0.625 0.532 -0.902 -0.902 RIsi3 -0.028 0.052 -0.544 0.586 -0.105 -0.105 RIsi4 -0.080 0.044 -1.833 0.067 -0.212 -0.212 RIsi5 -0.003 0.067 -0.048 0.962 -0.015 -0.015 RIsi6 -0.035 0.062 -0.569 0.569 -0.151 -0.151 RIadhd17 ~~
RIadhd18 0.293 0.022 13.215 0.000 0.555 0.555 RIsi1 -0.043 0.040 -1.066 0.286 -0.143 -0.143 RIsi2 -0.008 0.049 -0.163 0.870 -0.149 -0.149 RIsi3 0.014 0.038 0.364 0.716 0.045 0.045 RIsi4 -0.092 0.033 -2.752 0.006 -0.210 -0.210 RIsi5 0.027 0.050 0.539 0.590 0.108 0.108 RIsi6 -0.071 0.046 -1.524 0.128 -0.263 -0.263 RIadhd18 ~~
RIsi1 0.038 0.029 1.326 0.185 0.117 0.117 RIsi2 0.001 0.034 0.022 0.982 0.013 0.013 RIsi3 0.075 0.027 2.811 0.005 0.223 0.223 RIsi4 -0.091 0.024 -3.746 0.000 -0.191 -0.191 RIsi5 0.052 0.037 1.410 0.159 0.189 0.189 RIsi6 -0.131 0.032 -4.090 0.000 -0.448 -0.448 RIsi1 ~~
RIsi2 -0.056 0.086 -0.647 0.518 -1.664 -1.664 RIsi3 0.088 0.066 1.324 0.185 0.460 0.460 RIsi4 0.019 0.056 0.341 0.733 0.070 0.070 RIsi5 -0.030 0.085 -0.352 0.725 -0.194 -0.194 RIsi6 0.007 0.079 0.088 0.930 0.042 0.042 RIsi2 ~~
RIsi3 -0.115 0.082 -1.405 0.160 -3.343 -3.343 RIsi4 0.023 0.069 0.332 0.740 0.469 0.469 RIsi5 0.066 0.108 0.605 0.545 2.355 2.355 RIsi6 -0.044 0.099 -0.449 0.653 -1.487 -1.487 RIsi3 ~~
RIsi4 -0.113 0.053 -2.156 0.031 -0.408 -0.408 RIsi5 -0.092 0.081 -1.136 0.256 -0.581 -0.581 RIsi6 -0.111 0.075 -1.481 0.139 -0.650 -0.650 RIsi4 ~~
RIsi5 0.005 0.068 0.074 0.941 0.022 0.022 RIsi6 0.212 0.065 3.243 0.001 0.879 0.879 RIsi5 ~~
RIsi6 -0.029 0.098 -0.291 0.771 -0.207 -0.207

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.000 0.000 0.000 .pe81m7 0.000 0.000 0.000 .pe81m10 0.000 0.000 0.000 .pe81m12 0.000 0.000 0.000 .pe82m5 0.000 0.000 0.000 .pe82m7 0.000 0.000 0.000 .pe82m10 0.000 0.000 0.000 .pe82m12 0.000 0.000 0.000 .pe83m5 0.000 0.000 0.000 .pe83m7 0.000 0.000 0.000 .pe83m10 0.000 0.000 0.000 .pe83m12 0.000 0.000 0.000 .pe86m5 0.000 0.000 0.000 .pe86m7 0.000 0.000 0.000 .pe86m10 0.000 0.000 0.000 .pe86m12 0.000 0.000 0.000 .pe87m5 0.000 0.000 0.000 .pe87m7 0.000 0.000 0.000 .pe87m10 0.000 0.000 0.000 .pe87m12 0.000 0.000 0.000 .pe88m5 0.000 0.000 0.000 .pe88m7 0.000 0.000 0.000 .pe88m10 0.000 0.000 0.000 .pe88m12 0.000 0.000 0.000 .pe89m5 0.000 0.000 0.000 .pe89m7 0.000 0.000 0.000 .pe89m10 0.000 0.000 0.000 .pe89m12 0.000 0.000 0.000 .pe90m5 0.000 0.000 0.000 .pe90m7 0.000 0.000 0.000 .pe90m10 0.000 0.000 0.000 .pe90m12 0.000 0.000 0.000 .pe91m5 0.000 0.000 0.000 .pe91m7 0.000 0.000 0.000 .pe91m10 0.000 0.000 0.000 .pe91m12 0.000 0.000 0.000 .pe84m5 0.000 0.000 0.000 .pe84m7 0.000 0.000 0.000 .pe84m10 0.000 0.000 0.000 .pe84m12 0.000 0.000 0.000 .pe85m5 0.000 0.000 0.000 .pe85m7 0.000 0.000 0.000 .pe85m10 0.000 0.000 0.000 .pe85m12 0.000 0.000 0.000 .pe96m5 0.000 0.000 0.000 .pe96m7 0.000 0.000 0.000 .pe96m10 0.000 0.000 0.000 .pe96m12 0.000 0.000 0.000 .pe97m5 0.000 0.000 0.000 .pe97m7 0.000 0.000 0.000 .pe97m10 0.000 0.000 0.000 .pe97m12 0.000 0.000 0.000 .pe92m5 0.000 0.000 0.000 .pe92m7 0.000 0.000 0.000 .pe92m10 0.000 0.000 0.000 .pe92m12 0.000 0.000 0.000 .pe93m5 0.000 0.000 0.000 .pe93m7 0.000 0.000 0.000 .pe93m10 0.000 0.000 0.000 .pe93m12 0.000 0.000 0.000 .pe94m5 0.000 0.000 0.000 .pe94m7 0.000 0.000 0.000 .pe94m10 0.000 0.000 0.000 .pe94m12 0.000 0.000 0.000 .pe95m5 0.000 0.000 0.000 .pe95m7 0.000 0.000 0.000 .pe95m10 0.000 0.000 0.000 .pe95m12 0.000 0.000 0.000 .pe64m5 0.000 0.000 0.000 .pe64m7 0.000 0.000 0.000 .pe64m10 0.000 0.000 0.000 .pe64m12 0.000 0.000 0.000 .pe2m5 0.000 0.000 0.000 .pe2m7 0.000 0.000 0.000 .pe2m10 0.000 0.000 0.000 .pe2m12 0.000 0.000 0.000 .pe4m5 0.000 0.000 0.000 .pe4m7 0.000 0.000 0.000 .pe4m10 0.000 0.000 0.000 .pe4m12 0.000 0.000 0.000 .pe7m5 0.000 0.000 0.000 .pe7m7 0.000 0.000 0.000 .pe7m10 0.000 0.000 0.000 .pe7m12 0.000 0.000 0.000 .pe11m5 0.000 0.000 0.000 .pe11m7 0.000 0.000 0.000 .pe11m10 0.000 0.000 0.000 .pe11m12 0.000 0.000 0.000 .pe13m5 0.000 0.000 0.000 .pe13m7 0.000 0.000 0.000 .pe13m10 0.000 0.000 0.000 .pe13m12 0.000 0.000 0.000 .pe25m5 0.000 0.000 0.000 .pe25m7 0.000 0.000 0.000 .pe25m10 0.000 0.000 0.000 .pe25m12 0.000 0.000 0.000 RIadhd1 0.000 0.000 0.000 RIadhd2 0.000 0.000 0.000 RIadhd3 0.000 0.000 0.000 RIadhd4 0.000 0.000 0.000 RIadhd5 0.000 0.000 0.000 RIadhd6 0.000 0.000 0.000 RIadhd7 0.000 0.000 0.000 RIadhd8 0.000 0.000 0.000 RIadhd9 0.000 0.000 0.000 RIadhd10 0.000 0.000 0.000 RIadhd11 0.000 0.000 0.000 RIadhd12 0.000 0.000 0.000 RIadhd13 0.000 0.000 0.000 RIadhd14 0.000 0.000 0.000 RIadhd15 0.000 0.000 0.000 RIadhd16 0.000 0.000 0.000 RIadhd17 0.000 0.000 0.000 RIadhd18 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 .WFadhd7 0.000 0.000 0.000 .WFadhd10 0.000 0.000 0.000 .WFadhd12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5|t1 0.259 0.027 9.625 0.000 0.259 0.259 pe81m5|t2 1.097 0.033 32.979 0.000 1.097 1.097 pe81m7|t1 0.306 0.027 11.189 0.000 0.306 0.306 pe81m7|t2 1.182 0.035 33.839 0.000 1.182 1.182 pe81m10|t1 0.392 0.028 14.042 0.000 0.392 0.392 pe81m10|t2 1.214 0.036 33.924 0.000 1.214 1.214 pe81m12|t1 0.426 0.028 15.227 0.000 0.426 0.426 pe81m12|t2 1.251 0.036 34.365 0.000 1.251 1.251 pe82m5|t1 0.097 0.027 3.663 0.000 0.097 0.097 pe82m5|t2 1.070 0.033 32.543 0.000 1.070 1.070 pe82m7|t1 0.194 0.027 7.174 0.000 0.194 0.194 pe82m7|t2 1.258 0.036 34.728 0.000 1.258 1.258 pe82m10|t1 0.226 0.027 8.253 0.000 0.226 0.226 pe82m10|t2 1.328 0.038 35.067 0.000 1.328 1.328 pe82m12|t1 0.307 0.028 11.153 0.000 0.307 0.307 pe82m12|t2 1.369 0.039 35.399 0.000 1.369 1.369 pe83m5|t1 -0.110 0.027 -4.149 0.000 -0.110 -0.110 pe83m5|t2 0.763 0.030 25.804 0.000 0.763 0.763 pe83m7|t1 -0.025 0.027 -0.922 0.357 -0.025 -0.025 pe83m7|t2 0.963 0.032 30.148 0.000 0.963 0.963 pe83m10|t1 -0.025 0.027 -0.930 0.352 -0.025 -0.025 pe83m10|t2 1.012 0.033 30.830 0.000 1.012 1.012 pe83m12|t1 0.050 0.027 1.859 0.063 0.050 0.050 pe83m12|t2 1.096 0.034 32.309 0.000 1.096 1.096 pe86m5|t1 0.062 0.027 2.332 0.020 0.062 0.062 pe86m5|t2 1.139 0.034 33.602 0.000 1.139 1.139 pe86m7|t1 0.147 0.027 5.442 0.000 0.147 0.147 pe86m7|t2 1.307 0.037 35.202 0.000 1.307 1.307 pe86m10|t1 0.154 0.027 5.666 0.000 0.154 0.154 pe86m10|t2 1.286 0.037 34.682 0.000 1.286 1.286 pe86m12|t1 0.079 0.027 2.917 0.004 0.079 0.079 pe86m12|t2 1.340 0.038 35.188 0.000 1.340 1.340 pe87m5|t1 0.316 0.027 11.673 0.000 0.316 0.316 pe87m5|t2 1.496 0.041 36.708 0.000 1.496 1.496 pe87m7|t1 0.266 0.027 9.786 0.000 0.266 0.266 pe87m7|t2 1.639 0.045 36.304 0.000 1.639 1.639 pe87m10|t1 0.405 0.028 14.495 0.000 0.405 0.405 pe87m10|t2 1.764 0.050 35.500 0.000 1.764 1.764 pe87m12|t1 0.366 0.028 13.172 0.000 0.366 0.366 pe87m12|t2 1.697 0.047 35.855 0.000 1.697 1.697 pe88m5|t1 0.403 0.027 14.708 0.000 0.403 0.403 pe88m5|t2 1.138 0.034 33.544 0.000 1.138 1.138 pe88m7|t1 0.327 0.027 11.943 0.000 0.327 0.327 pe88m7|t2 1.283 0.037 34.958 0.000 1.283 1.283 pe88m10|t1 0.056 0.027 2.054 0.040 0.056 0.056 pe88m10|t2 1.014 0.033 30.866 0.000 1.014 1.014 pe88m12|t1 0.010 0.027 0.367 0.713 0.010 0.010 pe88m12|t2 0.942 0.032 29.482 0.000 0.942 0.942 pe89m5|t1 0.479 0.028 17.268 0.000 0.479 0.479 pe89m5|t2 1.290 0.036 35.432 0.000 1.290 1.290 pe89m7|t1 0.498 0.028 17.714 0.000 0.498 0.498 pe89m7|t2 1.408 0.039 35.908 0.000 1.408 1.408 pe89m10|t1 0.464 0.028 16.436 0.000 0.464 0.464 pe89m10|t2 1.399 0.039 35.550 0.000 1.399 1.399 pe89m12|t1 0.461 0.028 16.351 0.000 0.461 0.461 pe89m12|t2 1.383 0.039 35.458 0.000 1.383 1.383 pe90m5|t1 0.055 0.027 2.054 0.040 0.055 0.055 pe90m5|t2 0.807 0.030 26.937 0.000 0.807 0.807 pe90m7|t1 0.045 0.027 1.672 0.095 0.045 0.045 pe90m7|t2 0.987 0.032 30.641 0.000 0.987 0.987 pe90m10|t1 0.041 0.027 1.514 0.130 0.041 0.041 pe90m10|t2 0.938 0.032 29.392 0.000 0.938 0.938 pe90m12|t1 0.078 0.027 2.873 0.004 0.078 0.078 pe90m12|t2 0.969 0.032 30.035 0.000 0.969 0.969 pe91m5|t1 0.579 0.028 20.507 0.000 0.579 0.579 pe91m5|t2 1.443 0.040 36.534 0.000 1.443 1.443 pe91m7|t1 0.571 0.029 19.998 0.000 0.571 0.571 pe91m7|t2 1.534 0.042 36.342 0.000 1.534 1.534 pe91m10|t1 0.648 0.029 22.113 0.000 0.648 0.648 pe91m10|t2 1.605 0.045 36.045 0.000 1.605 1.605 pe91m12|t1 0.572 0.029 19.881 0.000 0.572 0.572 pe91m12|t2 1.623 0.045 36.040 0.000 1.623 1.623 pe84m5|t1 -0.002 0.027 -0.064 0.949 -0.002 -0.002 pe84m5|t2 0.973 0.032 30.690 0.000 0.973 0.973 pe84m7|t1 0.077 0.027 2.873 0.004 0.077 0.077 pe84m7|t2 1.232 0.036 34.421 0.000 1.232 1.232 pe84m10|t1 0.112 0.027 4.130 0.000 0.112 0.112 pe84m10|t2 1.257 0.037 34.403 0.000 1.257 1.257 pe84m12|t1 0.091 0.027 3.369 0.001 0.091 0.091 pe84m12|t2 1.296 0.037 34.821 0.000 1.296 1.296 pe85m5|t1 -0.932 0.031 -29.869 0.000 -0.932 -0.932 pe85m5|t2 0.266 0.027 9.897 0.000 0.266 0.266 pe85m7|t1 -0.865 0.031 -28.041 0.000 -0.865 -0.865 pe85m7|t2 0.586 0.029 20.490 0.000 0.586 0.586 pe85m10|t1 -0.480 0.028 -16.965 0.000 -0.480 -0.480 pe85m10|t2 0.865 0.031 27.768 0.000 0.865 0.865 pe85m12|t1 -0.290 0.028 -10.527 0.000 -0.290 -0.290 pe85m12|t2 0.951 0.032 29.673 0.000 0.951 0.951 pe96m5|t1 -0.188 0.027 -7.015 0.000 -0.188 -0.188 pe96m5|t2 0.849 0.030 27.946 0.000 0.849 0.849 pe96m7|t1 -0.169 0.027 -6.258 0.000 -0.169 -0.169 pe96m7|t2 0.923 0.031 29.308 0.000 0.923 0.923 pe96m10|t1 -0.036 0.027 -1.341 0.180 -0.036 -0.036 pe96m10|t2 1.072 0.034 31.883 0.000 1.072 1.072 pe96m12|t1 0.056 0.027 2.076 0.038 0.056 0.056 pe96m12|t2 1.117 0.034 32.620 0.000 1.117 1.117 pe97m5|t1 -0.358 0.027 -13.164 0.000 -0.358 -0.358 pe97m5|t2 0.606 0.028 21.331 0.000 0.606 0.606 pe97m7|t1 -0.268 0.027 -9.848 0.000 -0.268 -0.268 pe97m7|t2 0.792 0.030 26.251 0.000 0.792 0.792 pe97m10|t1 0.139 0.027 5.104 0.000 0.139 0.139 pe97m10|t2 1.110 0.034 32.496 0.000 1.110 1.110 pe97m12|t1 0.256 0.027 9.331 0.000 0.256 0.256 pe97m12|t2 1.169 0.035 33.361 0.000 1.169 1.169 pe92m5|t1 0.005 0.027 0.169 0.865 0.005 0.005 pe92m5|t2 0.809 0.030 27.001 0.000 0.809 0.809 pe92m7|t1 0.059 0.027 2.186 0.029 0.059 0.059 pe92m7|t2 0.959 0.032 30.087 0.000 0.959 0.959 pe92m10|t1 0.366 0.028 13.184 0.000 0.366 0.366 pe92m10|t2 1.153 0.035 33.129 0.000 1.153 1.153 pe92m12|t1 0.405 0.028 14.500 0.000 0.405 0.405 pe92m12|t2 1.248 0.036 34.338 0.000 1.248 1.248 pe93m5|t1 0.198 0.027 7.405 0.000 0.198 0.198 pe93m5|t2 0.874 0.031 28.582 0.000 0.874 0.874 pe93m7|t1 0.356 0.027 12.936 0.000 0.356 0.356 pe93m7|t2 1.003 0.032 30.945 0.000 1.003 1.003 pe93m10|t1 0.517 0.028 18.156 0.000 0.517 0.517 pe93m10|t2 1.240 0.036 34.221 0.000 1.240 1.240 pe93m12|t1 0.597 0.029 20.655 0.000 0.597 0.597 pe93m12|t2 1.298 0.037 34.836 0.000 1.298 1.298 pe94m5|t1 0.583 0.028 20.647 0.000 0.583 0.583 pe94m5|t2 1.230 0.035 34.837 0.000 1.230 1.230 pe94m7|t1 0.718 0.030 24.298 0.000 0.718 0.718 pe94m7|t2 1.375 0.038 35.717 0.000 1.375 1.375 pe94m10|t1 0.815 0.031 26.578 0.000 0.815 0.815 pe94m10|t2 1.556 0.043 36.053 0.000 1.556 1.556 pe94m12|t1 0.889 0.031 28.343 0.000 0.889 0.889 pe94m12|t2 1.589 0.044 36.072 0.000 1.589 1.589 pe95m5|t1 -0.006 0.027 -0.212 0.832 -0.006 -0.006 pe95m5|t2 0.509 0.028 18.286 0.000 0.509 0.509 pe95m7|t1 0.149 0.027 5.528 0.000 0.149 0.149 pe95m7|t2 0.767 0.030 25.609 0.000 0.767 0.767 pe95m10|t1 0.288 0.028 10.451 0.000 0.288 0.288 pe95m10|t2 0.940 0.032 29.430 0.000 0.940 0.940 pe95m12|t1 0.465 0.028 16.497 0.000 0.465 0.465 pe95m12|t2 1.079 0.034 32.012 0.000 1.079 1.079 pe64m5|t1 -0.246 0.027 -9.160 0.000 -0.246 -0.246 pe64m5|t2 0.491 0.028 17.686 0.000 0.491 0.491 pe64m7|t1 -0.283 0.027 -10.365 0.000 -0.283 -0.283 pe64m7|t2 0.635 0.029 21.939 0.000 0.635 0.635 pe64m10|t1 -0.075 0.027 -2.746 0.006 -0.075 -0.075 pe64m10|t2 0.869 0.031 27.872 0.000 0.869 0.869 pe64m12|t1 -0.046 0.027 -1.708 0.088 -0.046 -0.046 pe64m12|t2 0.872 0.031 27.939 0.000 0.872 0.872 pe2m5|t1 1.365 0.038 36.090 0.000 1.365 1.365 pe2m5|t2 2.367 0.082 28.719 0.000 2.367 2.367 pe2m7|t1 1.176 0.035 33.748 0.000 1.176 1.176 pe2m7|t2 2.259 0.075 30.173 0.000 2.259 2.259 pe2m10|t1 1.006 0.033 30.734 0.000 1.006 1.006 pe2m10|t2 2.135 0.067 31.753 0.000 2.135 2.135 pe2m12|t1 1.129 0.034 32.821 0.000 1.129 1.129 pe2m12|t2 2.238 0.074 30.252 0.000 2.238 2.238 pe4m5|t1 1.007 0.032 31.404 0.000 1.007 1.007 pe4m5|t2 2.130 0.066 32.506 0.000 2.130 2.130 pe4m7|t1 1.012 0.033 31.125 0.000 1.012 1.012 pe4m7|t2 2.259 0.075 30.173 0.000 2.259 2.259 pe4m10|t1 0.917 0.032 28.934 0.000 0.917 0.917 pe4m10|t2 2.147 0.068 31.589 0.000 2.147 2.147 pe4m12|t1 0.902 0.031 28.642 0.000 0.902 0.902 pe4m12|t2 2.224 0.073 30.475 0.000 2.224 2.224 pe7m5|t1 0.833 0.030 27.602 0.000 0.833 0.833 pe7m5|t2 1.958 0.056 34.655 0.000 1.958 1.958 pe7m7|t1 0.662 0.029 22.714 0.000 0.662 0.662 pe7m7|t2 1.831 0.052 35.391 0.000 1.831 1.831 pe7m10|t1 0.650 0.029 22.180 0.000 0.650 0.650 pe7m10|t2 1.940 0.057 34.118 0.000 1.940 1.940 pe7m12|t1 0.717 0.030 24.065 0.000 0.717 0.717 pe7m12|t2 2.006 0.060 33.431 0.000 2.006 2.006 pe11m5|t1 0.689 0.029 23.796 0.000 0.689 0.689 pe11m5|t2 1.784 0.049 36.154 0.000 1.784 1.784 pe11m7|t1 0.859 0.031 27.885 0.000 0.859 0.859 pe11m7|t2 1.890 0.054 34.916 0.000 1.890 1.890 pe11m10|t1 0.795 0.030 26.081 0.000 0.795 0.795 pe11m10|t2 1.980 0.059 33.697 0.000 1.980 1.980 pe11m12|t1 0.864 0.031 27.757 0.000 0.864 0.864 pe11m12|t2 1.997 0.060 33.525 0.000 1.997 1.997 pe13m5|t1 1.576 0.043 36.801 0.000 1.576 1.576 pe13m5|t2 2.649 0.112 23.551 0.000 2.649 2.649 pe13m7|t1 1.457 0.040 36.170 0.000 1.457 1.457 pe13m7|t2 2.605 0.108 24.096 0.000 2.605 2.605 pe13m10|t1 1.252 0.036 34.360 0.000 1.252 1.252 pe13m10|t2 2.390 0.086 27.724 0.000 2.390 2.390 pe13m12|t1 1.198 0.036 33.740 0.000 1.198 1.198 pe13m12|t2 2.283 0.077 29.527 0.000 2.283 2.283 pe25m5|t1 1.172 0.034 34.106 0.000 1.172 1.172 pe25m5|t2 2.283 0.076 30.143 0.000 2.283 2.283 pe25m7|t1 1.384 0.039 35.794 0.000 1.384 1.384 pe25m7|t2 2.322 0.080 29.135 0.000 2.322 2.322 pe25m10|t1 1.368 0.039 35.369 0.000 1.368 1.368 pe25m10|t2 2.333 0.081 28.695 0.000 2.333 2.333 pe25m12|t1 1.256 0.036 34.420 0.000 1.256 1.256 pe25m12|t2 2.457 0.092 26.575 0.000 2.457 2.457

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.229 0.229 0.229 .pe81m7 0.194 0.194 0.194 .pe81m10 0.177 0.177 0.177 .pe81m12 0.114 0.114 0.114 .pe82m5 0.317 0.317 0.317 .pe82m7 0.293 0.293 0.293 .pe82m10 0.271 0.271 0.271 .pe82m12 0.187 0.187 0.187 .pe83m5 0.245 0.245 0.245 .pe83m7 0.239 0.239 0.239 .pe83m10 0.200 0.200 0.200 .pe83m12 0.179 0.179 0.179 .pe86m5 0.338 0.338 0.338 .pe86m7 0.305 0.305 0.305 .pe86m10 0.330 0.330 0.330 .pe86m12 0.254 0.254 0.254 .pe87m5 0.460 0.460 0.460 .pe87m7 0.420 0.420 0.420 .pe87m10 0.375 0.375 0.375 .pe87m12 0.298 0.298 0.298 .pe88m5 0.400 0.400 0.400 .pe88m7 0.406 0.406 0.406 .pe88m10 0.413 0.413 0.413 .pe88m12 0.382 0.382 0.382 .pe89m5 0.350 0.350 0.350 .pe89m7 0.377 0.377 0.377 .pe89m10 0.377 0.377 0.377 .pe89m12 0.361 0.361 0.361 .pe90m5 0.389 0.389 0.389 .pe90m7 0.372 0.372 0.372 .pe90m10 0.435 0.435 0.435 .pe90m12 0.380 0.380 0.380 .pe91m5 0.374 0.374 0.374 .pe91m7 0.248 0.248 0.248 .pe91m10 0.327 0.327 0.327 .pe91m12 0.272 0.272 0.272 .pe84m5 0.386 0.386 0.386 .pe84m7 0.325 0.325 0.325 .pe84m10 0.270 0.270 0.270 .pe84m12 0.238 0.238 0.238 .pe85m5 0.417 0.417 0.417 .pe85m7 0.383 0.383 0.383 .pe85m10 0.300 0.300 0.300 .pe85m12 0.285 0.285 0.285 .pe96m5 0.558 0.558 0.558 .pe96m7 0.478 0.478 0.478 .pe96m10 0.348 0.348 0.348 .pe96m12 0.238 0.238 0.238 .pe97m5 0.407 0.407 0.407 .pe97m7 0.392 0.392 0.392 .pe97m10 0.319 0.319 0.319 .pe97m12 0.258 0.258 0.258 .pe92m5 0.274 0.274 0.274 .pe92m7 0.234 0.234 0.234 .pe92m10 0.184 0.184 0.184 .pe92m12 0.195 0.195 0.195 .pe93m5 0.182 0.182 0.182 .pe93m7 0.169 0.169 0.169 .pe93m10 0.179 0.179 0.179 .pe93m12 0.156 0.156 0.156 .pe94m5 0.210 0.210 0.210 .pe94m7 0.166 0.166 0.166 .pe94m10 0.207 0.207 0.207 .pe94m12 0.159 0.159 0.159 .pe95m5 0.294 0.294 0.294 .pe95m7 0.254 0.254 0.254 .pe95m10 0.323 0.323 0.323 .pe95m12 0.298 0.298 0.298 .pe64m5 0.360 0.360 0.360 .pe64m7 0.384 0.384 0.384 .pe64m10 0.316 0.316 0.316 .pe64m12 0.290 0.290 0.290 .pe2m5 0.488 0.488 0.488 .pe2m7 0.493 0.493 0.493 .pe2m10 0.429 0.429 0.429 .pe2m12 0.314 0.314 0.314 .pe4m5 0.231 0.231 0.231 .pe4m7 0.199 0.199 0.199 .pe4m10 0.280 0.280 0.280 .pe4m12 0.211 0.211 0.211 .pe7m5 0.557 0.557 0.557 .pe7m7 0.442 0.442 0.442 .pe7m10 0.401 0.401 0.401 .pe7m12 0.269 0.269 0.269 .pe11m5 0.356 0.356 0.356 .pe11m7 0.358 0.358 0.358 .pe11m10 0.410 0.410 0.410 .pe11m12 0.295 0.295 0.295 .pe13m5 0.236 0.236 0.236 .pe13m7 0.136 0.136 0.136 .pe13m10 0.157 0.157 0.157 .pe13m12 0.139 0.139 0.139 .pe25m5 0.287 0.287 0.287 .pe25m7 0.258 0.258 0.258 .pe25m10 0.264 0.264 0.264 .pe25m12 0.217 0.217 0.217 RIadhd1 0.359 0.053 6.821 0.000 1.000 1.000 RIadhd2 0.265 0.055 4.852 0.000 1.000 1.000 RIadhd3 0.285 0.056 5.068 0.000 1.000 1.000 RIadhd4 0.316 0.044 7.167 0.000 1.000 1.000 RIadhd5 0.376 0.032 11.920 0.000 1.000 1.000 RIadhd6 0.291 0.039 7.457 0.000 1.000 1.000 RIadhd7 0.335 0.039 8.519 0.000 1.000 1.000 RIadhd8 0.344 0.035 9.891 0.000 1.000 1.000 RIadhd9 0.371 0.043 8.702 0.000 1.000 1.000 RIadhd10 0.377 0.039 9.723 0.000 1.000 1.000 RIadhd11 0.359 0.036 9.967 0.000 1.000 1.000 RIadhd12 0.390 0.027 14.568 0.000 1.000 1.000 RIadhd13 0.358 0.036 10.063 0.000 1.000 1.000 RIadhd14 0.402 0.045 8.832 0.000 1.000 1.000 RIadhd15 0.399 0.051 7.865 0.000 1.000 1.000 RIadhd16 0.362 0.054 6.685 0.000 1.000 1.000 RIadhd17 0.483 0.030 15.862 0.000 1.000 1.000 RIadhd18 0.578 0.017 34.919 0.000 1.000 1.000 RIsi1 0.186 0.075 2.493 0.013 1.000 1.000 RIsi2 0.006 0.114 0.053 0.958 1.000 1.000 RIsi3 0.197 0.068 2.870 0.004 1.000 1.000 RIsi4 0.393 0.050 7.855 0.000 1.000 1.000 RIsi5 0.129 0.113 1.143 0.253 1.000 1.000 RIsi6 0.148 0.100 1.488 0.137 1.000 1.000 WFadhd5 0.412 0.055 7.484 0.000 1.000 1.000 .WFadhd7 0.264 0.018 14.389 0.000 0.589 0.589 .WFadhd10 0.269 0.018 15.316 0.000 0.579 0.579 .WFadhd12 0.199 0.013 14.989 0.000 0.377 0.377 WFsi5 0.326 0.091 3.583 0.000 1.000 1.000 .WFsi7 0.132 0.028 4.648 0.000 0.409 0.409 .WFsi10 0.146 0.026 5.663 0.000 0.379 0.379 .WFsi12 0.109 0.020 5.572 0.000 0.218 0.218

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5 1.000 1.000 1.000 pe81m7 1.000 1.000 1.000 pe81m10 1.000 1.000 1.000 pe81m12 1.000 1.000 1.000 pe82m5 1.000 1.000 1.000 pe82m7 1.000 1.000 1.000 pe82m10 1.000 1.000 1.000 pe82m12 1.000 1.000 1.000 pe83m5 1.000 1.000 1.000 pe83m7 1.000 1.000 1.000 pe83m10 1.000 1.000 1.000 pe83m12 1.000 1.000 1.000 pe86m5 1.000 1.000 1.000 pe86m7 1.000 1.000 1.000 pe86m10 1.000 1.000 1.000 pe86m12 1.000 1.000 1.000 pe87m5 1.000 1.000 1.000 pe87m7 1.000 1.000 1.000 pe87m10 1.000 1.000 1.000 pe87m12 1.000 1.000 1.000 pe88m5 1.000 1.000 1.000 pe88m7 1.000 1.000 1.000 pe88m10 1.000 1.000 1.000 pe88m12 1.000 1.000 1.000 pe89m5 1.000 1.000 1.000 pe89m7 1.000 1.000 1.000 pe89m10 1.000 1.000 1.000 pe89m12 1.000 1.000 1.000 pe90m5 1.000 1.000 1.000 pe90m7 1.000 1.000 1.000 pe90m10 1.000 1.000 1.000 pe90m12 1.000 1.000 1.000 pe91m5 1.000 1.000 1.000 pe91m7 1.000 1.000 1.000 pe91m10 1.000 1.000 1.000 pe91m12 1.000 1.000 1.000 pe84m5 1.000 1.000 1.000 pe84m7 1.000 1.000 1.000 pe84m10 1.000 1.000 1.000 pe84m12 1.000 1.000 1.000 pe85m5 1.000 1.000 1.000 pe85m7 1.000 1.000 1.000 pe85m10 1.000 1.000 1.000 pe85m12 1.000 1.000 1.000 pe96m5 1.000 1.000 1.000 pe96m7 1.000 1.000 1.000 pe96m10 1.000 1.000 1.000 pe96m12 1.000 1.000 1.000 pe97m5 1.000 1.000 1.000 pe97m7 1.000 1.000 1.000 pe97m10 1.000 1.000 1.000 pe97m12 1.000 1.000 1.000 pe92m5 1.000 1.000 1.000 pe92m7 1.000 1.000 1.000 pe92m10 1.000 1.000 1.000 pe92m12 1.000 1.000 1.000 pe93m5 1.000 1.000 1.000 pe93m7 1.000 1.000 1.000 pe93m10 1.000 1.000 1.000 pe93m12 1.000 1.000 1.000 pe94m5 1.000 1.000 1.000 pe94m7 1.000 1.000 1.000 pe94m10 1.000 1.000 1.000 pe94m12 1.000 1.000 1.000 pe95m5 1.000 1.000 1.000 pe95m7 1.000 1.000 1.000 pe95m10 1.000 1.000 1.000 pe95m12 1.000 1.000 1.000 pe64m5 1.000 1.000 1.000 pe64m7 1.000 1.000 1.000 pe64m10 1.000 1.000 1.000 pe64m12 1.000 1.000 1.000 pe2m5 1.000 1.000 1.000 pe2m7 1.000 1.000 1.000 pe2m10 1.000 1.000 1.000 pe2m12 1.000 1.000 1.000 pe4m5 1.000 1.000 1.000 pe4m7 1.000 1.000 1.000 pe4m10 1.000 1.000 1.000 pe4m12 1.000 1.000 1.000 pe7m5 1.000 1.000 1.000 pe7m7 1.000 1.000 1.000 pe7m10 1.000 1.000 1.000 pe7m12 1.000 1.000 1.000 pe11m5 1.000 1.000 1.000 pe11m7 1.000 1.000 1.000 pe11m10 1.000 1.000 1.000 pe11m12 1.000 1.000 1.000 pe13m5 1.000 1.000 1.000 pe13m7 1.000 1.000 1.000 pe13m10 1.000 1.000 1.000 pe13m12 1.000 1.000 1.000 pe25m5 1.000 1.000 1.000 pe25m7 1.000 1.000 1.000 pe25m10 1.000 1.000 1.000 pe25m12 1.000 1.000 1.000

S1 Model fit: Comparative Fit Index (CFI) 0.986 (>0.95) Tucker-Lewis Index (TLI) 0.984 (>0.95)
RMSEA 0.016 (≤ 0.06)
90 Percent confidence interval - lower 0.015 90 Percent confidence interval - upper 0.017
SRMR 0.033 (≤ 0.08)

We can conclude that the model shows very good fit.

RICLPM_multi_adhd_S2: ADHD step 2

Multiple response items RICLPM mother report ADHD symptoms and social isolation: Step 2.

In our second step model, we constrain the factor loadings to be invariant over time using the labels a*, b*, c*, d* etc, in the “within” part of the model.

RICLPM_multi_adhd_S2 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of ADHD (mother report)
  # Inattention symptoms
  RIadhd1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIadhd2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIadhd3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIadhd4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIadhd5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIadhd6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIadhd7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIadhd8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIadhd9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  #Hyperactivity symptoms
  RIadhd10 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIadhd11 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIadhd12 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIadhd13 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIadhd14 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIadhd15 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIadhd16 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIadhd17 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIadhd18 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for ADHD (inattention and hyperactivity) symptoms at 4 waves
  WFadhd5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5 + j*pe84m5 + k*pe85m5 + l*pe96m5 + m*pe97m5 + n*pe92m5 + o*pe93m5 + p*pe94m5 + q*pe95m5 + r*pe64m5
  WFadhd7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7 + j*pe84m7 + k*pe85m7 + l*pe96m7 + m*pe97m7 + n*pe92m7 + o*pe93m7 + p*pe94m7 + q*pe95m7 + r*pe64m7
  WFadhd10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10 + j*pe84m10 + k*pe85m10 + l*pe96m10 + m*pe97m10 + n*pe92m10 + o*pe93m10 + p*pe94m10 + q*pe95m10 + r*pe64m10
  WFadhd12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 + j*pe84m12 + k*pe85m12 + l*pe96m12 + m*pe97m12 + n*pe92m12 + o*pe93m12 + p*pe94m12 + q*pe95m12 + r*pe64m12 
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ s*pe2m5 + t*pe4m5 + u*pe7m5 + v*pe11m5 + w*pe13m5 + x*pe25m5 
  WFsi7 =~ s*pe2m7 + t*pe4m7 + u*pe7m7 + v*pe11m7 + w*pe13m7 + x*pe25m7 
  WFsi10 =~ s*pe2m10 + t*pe4m10 + u*pe7m10 + v*pe11m10 + w*pe13m10 + x*pe25m10
  WFsi12 =~ s*pe2m12 + t*pe4m12 + u*pe7m12 + v*pe11m12 + w*pe13m12 + x*pe25m12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFadhd7 + WFsi7 ~ WFadhd5 + WFsi5
  WFadhd10 + WFsi10 ~ WFadhd7 + WFsi7
  WFadhd12 + WFsi12 ~ WFadhd10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFadhd5 ~~ WFsi5
  WFadhd7 ~~ WFsi7
  WFadhd10 ~~ WFsi10 
  WFadhd12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIadhd1 + RIadhd2 + RIadhd3 + RIadhd4 + RIadhd5 + RIadhd6 + RIadhd7 + RIadhd8 + RIadhd9 + RIadhd10 + RIadhd11 + RIadhd12 + RIadhd13 + RIadhd14 + RIadhd15 + RIadhd16 + RIadhd17 + RIadhd18 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFadhd5
'

RICLPM_multi_adhd_S2.fit <- cfa(RICLPM_multi_adhd_S2, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPM_multi_adhd_S2.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 322 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 604 Number of equality constraints 66

Number of observations 2232 Number of missing patterns 70

Model Test User Model: Standard Robust Test Statistic 8153.127 6806.703 Degrees of freedom 4214 4214 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.030 Shift parameter 2790.292 simple second-order correction

Model Test Baseline Model:

Test statistic 953296.763 166852.672 Degrees of freedom 4560 4560 P-value 0.000 0.000 Scaling correction factor 5.846

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.996 0.984 Tucker-Lewis Index (TLI) 0.996 0.983

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.020 0.017 90 Percent confidence interval - lower 0.020 0.016 90 Percent confidence interval - upper 0.021 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.037 0.037

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIadhd1 =~
pe81m5 1.000 0.715 0.715 pe81m7 1.000 0.715 0.715 pe81m10 1.000 0.715 0.715 pe81m12 1.000 0.715 0.715 RIadhd2 =~
pe82m5 1.000 0.644 0.644 pe82m7 1.000 0.644 0.644 pe82m10 1.000 0.644 0.644 pe82m12 1.000 0.644 0.644 RIadhd3 =~
pe83m5 1.000 0.672 0.672 pe83m7 1.000 0.672 0.672 pe83m10 1.000 0.672 0.672 pe83m12 1.000 0.672 0.672 RIadhd4 =~
pe86m5 1.000 0.663 0.663 pe86m7 1.000 0.663 0.663 pe86m10 1.000 0.663 0.663 pe86m12 1.000 0.663 0.663 RIadhd5 =~
pe87m5 1.000 0.666 0.666 pe87m7 1.000 0.666 0.666 pe87m10 1.000 0.666 0.666 pe87m12 1.000 0.666 0.666 RIadhd6 =~
pe88m5 1.000 0.627 0.627 pe88m7 1.000 0.627 0.627 pe88m10 1.000 0.627 0.627 pe88m12 1.000 0.627 0.627 RIadhd7 =~
pe89m5 1.000 0.662 0.662 pe89m7 1.000 0.662 0.662 pe89m10 1.000 0.662 0.662 pe89m12 1.000 0.662 0.662 RIadhd8 =~
pe90m5 1.000 0.657 0.657 pe90m7 1.000 0.657 0.657 pe90m10 1.000 0.657 0.657 pe90m12 1.000 0.657 0.657 RIadhd9 =~
pe91m5 1.000 0.690 0.690 pe91m7 1.000 0.690 0.690 pe91m10 1.000 0.690 0.690 pe91m12 1.000 0.690 0.690 RIadhd10 =~
pe84m5 1.000 0.687 0.687 pe84m7 1.000 0.687 0.687 pe84m10 1.000 0.687 0.687 pe84m12 1.000 0.687 0.687 RIadhd11 =~
pe85m5 1.000 0.667 0.667 pe85m7 1.000 0.667 0.667 pe85m10 1.000 0.667 0.667 pe85m12 1.000 0.667 0.667 RIadhd12 =~
pe96m5 1.000 0.643 0.643 pe96m7 1.000 0.643 0.643 pe96m10 1.000 0.643 0.643 pe96m12 1.000 0.643 0.643 RIadhd13 =~
pe97m5 1.000 0.665 0.665 pe97m7 1.000 0.665 0.665 pe97m10 1.000 0.665 0.665 pe97m12 1.000 0.665 0.665 RIadhd14 =~
pe92m5 1.000 0.725 0.725 pe92m7 1.000 0.725 0.725 pe92m10 1.000 0.725 0.725 pe92m12 1.000 0.725 0.725 RIadhd15 =~
pe93m5 1.000 0.740 0.740 pe93m7 1.000 0.740 0.740 pe93m10 1.000 0.740 0.740 pe93m12 1.000 0.740 0.740 RIadhd16 =~
pe94m5 1.000 0.721 0.721 pe94m7 1.000 0.721 0.721 pe94m10 1.000 0.721 0.721 pe94m12 1.000 0.721 0.721 RIadhd17 =~
pe95m5 1.000 0.751 0.751 pe95m7 1.000 0.751 0.751 pe95m10 1.000 0.751 0.751 pe95m12 1.000 0.751 0.751 RIadhd18 =~
pe64m5 1.000 0.773 0.773 pe64m7 1.000 0.773 0.773 pe64m10 1.000 0.773 0.773 pe64m12 1.000 0.773 0.773 RIsi1 =~
pe2m5 1.000 0.504 0.504 pe2m7 1.000 0.504 0.504 pe2m10 1.000 0.504 0.504 pe2m12 1.000 0.504 0.504 RIsi2 =~
pe4m5 1.000 0.552 0.552 pe4m7 1.000 0.552 0.552 pe4m10 1.000 0.552 0.552 pe4m12 1.000 0.552 0.552 RIsi3 =~
pe7m5 1.000 0.471 0.471 pe7m7 1.000 0.471 0.471 pe7m10 1.000 0.471 0.471 pe7m12 1.000 0.471 0.471 RIsi4 =~
pe11m5 1.000 0.689 0.689 pe11m7 1.000 0.689 0.689 pe11m10 1.000 0.689 0.689 pe11m12 1.000 0.689 0.689 RIsi5 =~
pe13m5 1.000 0.627 0.627 pe13m7 1.000 0.627 0.627 pe13m10 1.000 0.627 0.627 pe13m12 1.000 0.627 0.627 RIsi6 =~
pe25m5 1.000 0.607 0.607 pe25m7 1.000 0.607 0.607 pe25m10 1.000 0.607 0.607 pe25m12 1.000 0.607 0.607 WFadhd5 =~
pe81m5 (a) 1.000 0.495 0.495 pe82m5 (b) 1.021 0.030 34.200 0.000 0.506 0.506 pe83m5 (c) 1.026 0.026 39.105 0.000 0.508 0.508 pe86m5 (d) 0.901 0.032 28.174 0.000 0.446 0.446 pe87m5 (e) 0.780 0.038 20.757 0.000 0.387 0.387 pe88m5 (f) 0.803 0.038 21.214 0.000 0.398 0.398 pe89m5 (g) 0.767 0.037 20.981 0.000 0.380 0.380 pe90m5 (h) 0.727 0.038 18.967 0.000 0.360 0.360 pe91m5 (i) 0.845 0.039 21.832 0.000 0.419 0.419 pe84m5 (j) 0.884 0.034 25.989 0.000 0.438 0.438 pe85m5 (k) 0.866 0.035 24.479 0.000 0.429 0.429 pe96m5 (l) 0.887 0.041 21.882 0.000 0.439 0.439 pe97m5 (m) 0.878 0.036 24.587 0.000 0.435 0.435 pe92m5 (n) 0.910 0.033 27.444 0.000 0.451 0.451 pe93m5 (o) 0.923 0.030 30.317 0.000 0.457 0.457 pe94m5 (p) 0.949 0.035 27.145 0.000 0.470 0.470 pe95m5 (q) 0.637 0.035 18.171 0.000 0.316 0.316 pe64m5 (r) 0.496 0.037 13.386 0.000 0.246 0.246 WFadhd7 =~
pe81m7 (a) 1.000 0.536 0.536 pe82m7 (b) 1.021 0.030 34.200 0.000 0.548 0.548 pe83m7 (c) 1.026 0.026 39.105 0.000 0.550 0.550 pe86m7 (d) 0.901 0.032 28.174 0.000 0.483 0.483 pe87m7 (e) 0.780 0.038 20.757 0.000 0.418 0.418 pe88m7 (f) 0.803 0.038 21.214 0.000 0.430 0.430 pe89m7 (g) 0.767 0.037 20.981 0.000 0.411 0.411 pe90m7 (h) 0.727 0.038 18.967 0.000 0.390 0.390 pe91m7 (i) 0.845 0.039 21.832 0.000 0.453 0.453 pe84m7 (j) 0.884 0.034 25.989 0.000 0.474 0.474 pe85m7 (k) 0.866 0.035 24.479 0.000 0.464 0.464 pe96m7 (l) 0.887 0.041 21.882 0.000 0.475 0.475 pe97m7 (m) 0.878 0.036 24.587 0.000 0.471 0.471 pe92m7 (n) 0.910 0.033 27.444 0.000 0.488 0.488 pe93m7 (o) 0.923 0.030 30.317 0.000 0.495 0.495 pe94m7 (p) 0.949 0.035 27.145 0.000 0.509 0.509 pe95m7 (q) 0.637 0.035 18.171 0.000 0.342 0.342 pe64m7 (r) 0.496 0.037 13.386 0.000 0.266 0.266 WFadhd10 =~
pe81m10 (a) 1.000 0.555 0.555 pe82m10 (b) 1.021 0.030 34.200 0.000 0.567 0.567 pe83m10 (c) 1.026 0.026 39.105 0.000 0.569 0.569 pe86m10 (d) 0.901 0.032 28.174 0.000 0.500 0.500 pe87m10 (e) 0.780 0.038 20.757 0.000 0.433 0.433 pe88m10 (f) 0.803 0.038 21.214 0.000 0.445 0.445 pe89m10 (g) 0.767 0.037 20.981 0.000 0.425 0.425 pe90m10 (h) 0.727 0.038 18.967 0.000 0.403 0.403 pe91m10 (i) 0.845 0.039 21.832 0.000 0.469 0.469 pe84m10 (j) 0.884 0.034 25.989 0.000 0.490 0.490 pe85m10 (k) 0.866 0.035 24.479 0.000 0.480 0.480 pe96m10 (l) 0.887 0.041 21.882 0.000 0.492 0.492 pe97m10 (m) 0.878 0.036 24.587 0.000 0.487 0.487 pe92m10 (n) 0.910 0.033 27.444 0.000 0.505 0.505 pe93m10 (o) 0.923 0.030 30.317 0.000 0.512 0.512 pe94m10 (p) 0.949 0.035 27.145 0.000 0.526 0.526 pe95m10 (q) 0.637 0.035 18.171 0.000 0.354 0.354 pe64m10 (r) 0.496 0.037 13.386 0.000 0.275 0.275 WFadhd12 =~
pe81m12 (a) 1.000 0.608 0.608 pe82m12 (b) 1.021 0.030 34.200 0.000 0.621 0.621 pe83m12 (c) 1.026 0.026 39.105 0.000 0.624 0.624 pe86m12 (d) 0.901 0.032 28.174 0.000 0.547 0.547 pe87m12 (e) 0.780 0.038 20.757 0.000 0.474 0.474 pe88m12 (f) 0.803 0.038 21.214 0.000 0.488 0.488 pe89m12 (g) 0.767 0.037 20.981 0.000 0.466 0.466 pe90m12 (h) 0.727 0.038 18.967 0.000 0.442 0.442 pe91m12 (i) 0.845 0.039 21.832 0.000 0.514 0.514 pe84m12 (j) 0.884 0.034 25.989 0.000 0.537 0.537 pe85m12 (k) 0.866 0.035 24.479 0.000 0.526 0.526 pe96m12 (l) 0.887 0.041 21.882 0.000 0.539 0.539 pe97m12 (m) 0.878 0.036 24.587 0.000 0.533 0.533 pe92m12 (n) 0.910 0.033 27.444 0.000 0.553 0.553 pe93m12 (o) 0.923 0.030 30.317 0.000 0.561 0.561 pe94m12 (p) 0.949 0.035 27.145 0.000 0.577 0.577 pe95m12 (q) 0.637 0.035 18.171 0.000 0.387 0.387 pe64m12 (r) 0.496 0.037 13.386 0.000 0.301 0.301 WFsi5 =~
pe2m5 (s) 1.000 0.558 0.558 pe4m5 (t) 1.097 0.100 10.939 0.000 0.612 0.612 pe7m5 (u) 1.105 0.105 10.486 0.000 0.616 0.616 pe11m5 (v) 0.684 0.085 8.006 0.000 0.382 0.382 pe13m5 (w) 1.090 0.112 9.772 0.000 0.608 0.608 pe25m5 (x) 1.003 0.105 9.541 0.000 0.559 0.559 WFsi7 =~
pe2m7 (s) 1.000 0.623 0.623 pe4m7 (t) 1.097 0.100 10.939 0.000 0.683 0.683 pe7m7 (u) 1.105 0.105 10.486 0.000 0.688 0.688 pe11m7 (v) 0.684 0.085 8.006 0.000 0.426 0.426 pe13m7 (w) 1.090 0.112 9.772 0.000 0.678 0.678 pe25m7 (x) 1.003 0.105 9.541 0.000 0.625 0.625 WFsi10 =~
pe2m10 (s) 1.000 0.617 0.617 pe4m10 (t) 1.097 0.100 10.939 0.000 0.677 0.677 pe7m10 (u) 1.105 0.105 10.486 0.000 0.682 0.682 pe11m10 (v) 0.684 0.085 8.006 0.000 0.422 0.422 pe13m10 (w) 1.090 0.112 9.772 0.000 0.672 0.672 pe25m10 (x) 1.003 0.105 9.541 0.000 0.619 0.619 WFsi12 =~
pe2m12 (s) 1.000 0.652 0.652 pe4m12 (t) 1.097 0.100 10.939 0.000 0.715 0.715 pe7m12 (u) 1.105 0.105 10.486 0.000 0.720 0.720 pe11m12 (v) 0.684 0.085 8.006 0.000 0.446 0.446 pe13m12 (w) 1.090 0.112 9.772 0.000 0.710 0.710 pe25m12 (x) 1.003 0.105 9.541 0.000 0.654 0.654

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd7 ~
WFadhd5 0.420 0.057 7.364 0.000 0.388 0.388 WFsi5 -0.090 0.064 -1.403 0.161 -0.093 -0.093 WFsi7 ~
WFadhd5 -0.057 0.075 -0.759 0.448 -0.045 -0.045 WFsi5 0.679 0.075 9.001 0.000 0.608 0.608 WFadhd10 ~
WFadhd7 0.450 0.052 8.602 0.000 0.435 0.435 WFsi7 -0.020 0.061 -0.330 0.741 -0.023 -0.023 WFsi10 ~
WFadhd7 0.010 0.062 0.154 0.877 0.008 0.008 WFsi7 0.600 0.075 8.032 0.000 0.605 0.605 WFadhd12 ~
WFadhd10 0.775 0.031 24.949 0.000 0.707 0.707 WFsi10 -0.049 0.039 -1.284 0.199 -0.050 -0.050 WFsi12 ~
WFadhd10 0.084 0.047 1.794 0.073 0.071 0.071 WFsi10 0.791 0.046 17.310 0.000 0.748 0.748

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd5 ~~
WFsi5 0.038 0.027 1.375 0.169 0.136 0.136 .WFadhd7 ~~
.WFsi7 0.088 0.015 5.810 0.000 0.359 0.359 .WFadhd10 ~~
.WFsi10 0.071 0.013 5.260 0.000 0.289 0.289 .WFadhd12 ~~
.WFsi12 0.080 0.011 6.954 0.000 0.439 0.439 RIadhd1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd7 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd8 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd9 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd10 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd11 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd12 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd13 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd14 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd15 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd16 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd17 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd18 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd1 ~~
RIadhd2 0.451 0.023 19.566 0.000 0.979 0.979 RIadhd3 0.499 0.023 22.007 0.000 1.039 1.039 RIadhd4 0.385 0.022 17.566 0.000 0.812 0.812 RIadhd5 0.244 0.022 11.358 0.000 0.514 0.514 RIadhd6 0.274 0.021 13.053 0.000 0.613 0.613 RIadhd7 0.393 0.020 19.266 0.000 0.832 0.832 RIadhd8 0.171 0.021 8.063 0.000 0.364 0.364 RIadhd9 0.325 0.023 14.227 0.000 0.659 0.659 RIadhd10 0.331 0.022 15.353 0.000 0.674 0.674 RIadhd11 0.230 0.022 10.605 0.000 0.482 0.482 RIadhd12 0.075 0.022 3.353 0.001 0.163 0.163 RIadhd13 0.221 0.022 9.909 0.000 0.466 0.466 RIadhd14 0.348 0.023 15.377 0.000 0.673 0.673 RIadhd15 0.411 0.023 17.774 0.000 0.777 0.777 RIadhd16 0.450 0.024 19.052 0.000 0.873 0.873 RIadhd17 0.309 0.021 15.064 0.000 0.576 0.576 RIadhd18 0.157 0.020 7.850 0.000 0.284 0.284 RIsi1 0.156 0.030 5.148 0.000 0.433 0.433 RIsi2 0.264 0.032 8.373 0.000 0.669 0.669 RIsi3 0.149 0.032 4.716 0.000 0.442 0.442 RIsi4 0.110 0.024 4.582 0.000 0.224 0.224 RIsi5 0.297 0.033 8.954 0.000 0.662 0.662 RIsi6 0.261 0.031 8.527 0.000 0.601 0.601 RIadhd2 ~~
RIadhd3 0.416 0.023 17.969 0.000 0.960 0.960 RIadhd4 0.365 0.022 16.524 0.000 0.856 0.856 RIadhd5 0.205 0.022 9.493 0.000 0.478 0.478 RIadhd6 0.275 0.021 13.248 0.000 0.681 0.681 RIadhd7 0.370 0.021 17.979 0.000 0.868 0.868 RIadhd8 0.189 0.020 9.288 0.000 0.447 0.447 RIadhd9 0.295 0.023 13.085 0.000 0.664 0.664 RIadhd10 0.291 0.022 13.486 0.000 0.657 0.657 RIadhd11 0.192 0.021 9.002 0.000 0.447 0.447 RIadhd12 0.050 0.021 2.313 0.021 0.120 0.120 RIadhd13 0.180 0.022 8.193 0.000 0.420 0.420 RIadhd14 0.260 0.023 11.407 0.000 0.558 0.558 RIadhd15 0.300 0.024 12.744 0.000 0.630 0.630 RIadhd16 0.369 0.024 15.414 0.000 0.795 0.795 RIadhd17 0.215 0.020 10.552 0.000 0.444 0.444 RIadhd18 0.098 0.019 5.057 0.000 0.197 0.197 RIsi1 0.126 0.030 4.166 0.000 0.387 0.387 RIsi2 0.224 0.032 7.025 0.000 0.631 0.631 RIsi3 0.129 0.032 4.076 0.000 0.426 0.426 RIsi4 0.093 0.024 3.819 0.000 0.209 0.209 RIsi5 0.244 0.034 7.188 0.000 0.603 0.603 RIsi6 0.223 0.032 7.056 0.000 0.571 0.571 RIadhd3 ~~
RIadhd4 0.367 0.022 16.680 0.000 0.824 0.824 RIadhd5 0.230 0.021 10.755 0.000 0.514 0.514 RIadhd6 0.261 0.021 12.577 0.000 0.620 0.620 RIadhd7 0.350 0.021 16.955 0.000 0.788 0.788 RIadhd8 0.180 0.021 8.631 0.000 0.407 0.407 RIadhd9 0.309 0.023 13.679 0.000 0.666 0.666 RIadhd10 0.357 0.021 16.718 0.000 0.773 0.773 RIadhd11 0.251 0.021 11.775 0.000 0.559 0.559 RIadhd12 0.114 0.022 5.216 0.000 0.265 0.265 RIadhd13 0.235 0.022 10.784 0.000 0.527 0.527 RIadhd14 0.332 0.023 14.697 0.000 0.681 0.681 RIadhd15 0.381 0.023 16.473 0.000 0.766 0.766 RIadhd16 0.422 0.024 17.890 0.000 0.871 0.871 RIadhd17 0.317 0.020 16.021 0.000 0.629 0.629 RIadhd18 0.194 0.019 10.335 0.000 0.374 0.374 RIsi1 0.141 0.030 4.651 0.000 0.415 0.415 RIsi2 0.232 0.031 7.451 0.000 0.625 0.625 RIsi3 0.142 0.032 4.497 0.000 0.448 0.448 RIsi4 0.084 0.024 3.546 0.000 0.182 0.182 RIsi5 0.269 0.033 8.161 0.000 0.637 0.637 RIsi6 0.233 0.030 7.700 0.000 0.571 0.571 RIadhd4 ~~
RIadhd5 0.260 0.020 12.793 0.000 0.590 0.590 RIadhd6 0.309 0.020 15.488 0.000 0.743 0.743 RIadhd7 0.372 0.020 18.920 0.000 0.848 0.848 RIadhd8 0.219 0.020 11.126 0.000 0.502 0.502 RIadhd9 0.310 0.022 14.407 0.000 0.678 0.678 RIadhd10 0.343 0.020 16.842 0.000 0.753 0.753 RIadhd11 0.268 0.020 13.372 0.000 0.606 0.606 RIadhd12 0.122 0.021 5.903 0.000 0.287 0.287 RIadhd13 0.229 0.021 11.009 0.000 0.520 0.520 RIadhd14 0.282 0.022 12.964 0.000 0.587 0.587 RIadhd15 0.321 0.022 14.403 0.000 0.655 0.655 RIadhd16 0.353 0.023 15.521 0.000 0.738 0.738 RIadhd17 0.238 0.020 12.154 0.000 0.477 0.477 RIadhd18 0.154 0.019 8.293 0.000 0.301 0.301 RIsi1 0.123 0.028 4.432 0.000 0.368 0.368 RIsi2 0.241 0.029 8.330 0.000 0.659 0.659 RIsi3 0.148 0.029 5.125 0.000 0.474 0.474 RIsi4 0.079 0.023 3.457 0.001 0.172 0.172 RIsi5 0.274 0.031 8.927 0.000 0.659 0.659 RIsi6 0.218 0.028 7.653 0.000 0.541 0.541 RIadhd5 ~~
RIadhd6 0.267 0.019 14.059 0.000 0.641 0.641 RIadhd7 0.238 0.020 12.160 0.000 0.541 0.541 RIadhd8 0.194 0.019 10.072 0.000 0.444 0.444 RIadhd9 0.319 0.021 15.131 0.000 0.696 0.696 RIadhd10 0.252 0.020 12.576 0.000 0.551 0.551 RIadhd11 0.221 0.019 11.499 0.000 0.497 0.497 RIadhd12 0.101 0.020 5.155 0.000 0.236 0.236 RIadhd13 0.185 0.020 9.292 0.000 0.419 0.419 RIadhd14 0.219 0.021 10.455 0.000 0.455 0.455 RIadhd15 0.236 0.022 10.887 0.000 0.478 0.478 RIadhd16 0.240 0.022 10.757 0.000 0.500 0.500 RIadhd17 0.174 0.019 8.951 0.000 0.348 0.348 RIadhd18 0.114 0.018 6.206 0.000 0.221 0.221 RIsi1 0.182 0.026 6.918 0.000 0.543 0.543 RIsi2 0.263 0.027 9.752 0.000 0.716 0.716 RIsi3 0.132 0.026 4.991 0.000 0.420 0.420 RIsi4 0.202 0.022 9.220 0.000 0.441 0.441 RIsi5 0.302 0.029 10.348 0.000 0.724 0.724 RIsi6 0.279 0.027 10.427 0.000 0.692 0.692 RIadhd6 ~~
RIadhd7 0.291 0.019 15.239 0.000 0.701 0.701 RIadhd8 0.346 0.018 18.768 0.000 0.840 0.840 RIadhd9 0.325 0.021 15.654 0.000 0.753 0.753 RIadhd10 0.266 0.019 13.690 0.000 0.617 0.617 RIadhd11 0.188 0.019 9.890 0.000 0.451 0.451 RIadhd12 0.066 0.019 3.468 0.001 0.164 0.164 RIadhd13 0.157 0.020 8.011 0.000 0.378 0.378 RIadhd14 0.212 0.021 10.204 0.000 0.468 0.468 RIadhd15 0.225 0.021 10.594 0.000 0.486 0.486 RIadhd16 0.242 0.022 10.945 0.000 0.535 0.535 RIadhd17 0.146 0.018 7.896 0.000 0.309 0.309 RIadhd18 0.100 0.017 5.790 0.000 0.207 0.207 RIsi1 0.096 0.027 3.568 0.000 0.303 0.303 RIsi2 0.222 0.026 8.417 0.000 0.643 0.643 RIsi3 0.115 0.027 4.300 0.000 0.388 0.388 RIsi4 0.090 0.021 4.283 0.000 0.208 0.208 RIsi5 0.245 0.029 8.518 0.000 0.624 0.624 RIsi6 0.196 0.027 7.353 0.000 0.515 0.515 RIadhd7 ~~
RIadhd8 0.183 0.019 9.563 0.000 0.422 0.422 RIadhd9 0.292 0.021 13.870 0.000 0.639 0.639 RIadhd10 0.257 0.020 12.931 0.000 0.564 0.564 RIadhd11 0.185 0.019 9.629 0.000 0.420 0.420 RIadhd12 0.037 0.019 1.901 0.057 0.087 0.087 RIadhd13 0.173 0.020 8.703 0.000 0.394 0.394 RIadhd14 0.252 0.021 12.122 0.000 0.527 0.527 RIadhd15 0.280 0.021 13.245 0.000 0.572 0.572 RIadhd16 0.328 0.022 15.077 0.000 0.688 0.688 RIadhd17 0.163 0.019 8.512 0.000 0.328 0.328 RIadhd18 0.057 0.019 3.039 0.002 0.111 0.111 RIsi1 0.164 0.026 6.381 0.000 0.493 0.493 RIsi2 0.253 0.027 9.318 0.000 0.694 0.694 RIsi3 0.156 0.026 5.919 0.000 0.500 0.500 RIsi4 0.146 0.022 6.808 0.000 0.321 0.321 RIsi5 0.277 0.030 9.314 0.000 0.668 0.668 RIsi6 0.295 0.027 11.078 0.000 0.735 0.735 RIadhd8 ~~
RIadhd9 0.327 0.020 16.118 0.000 0.721 0.721 RIadhd10 0.239 0.019 12.408 0.000 0.529 0.529 RIadhd11 0.209 0.019 11.120 0.000 0.476 0.476 RIadhd12 0.108 0.019 5.681 0.000 0.255 0.255 RIadhd13 0.151 0.019 7.811 0.000 0.345 0.345 RIadhd14 0.184 0.020 9.093 0.000 0.387 0.387 RIadhd15 0.171 0.021 8.207 0.000 0.351 0.351 RIadhd16 0.168 0.022 7.761 0.000 0.354 0.354 RIadhd17 0.120 0.018 6.488 0.000 0.243 0.243 RIadhd18 0.130 0.017 7.536 0.000 0.256 0.256 RIsi1 0.129 0.026 4.987 0.000 0.390 0.390 RIsi2 0.208 0.026 7.969 0.000 0.574 0.574 RIsi3 0.119 0.026 4.595 0.000 0.383 0.383 RIsi4 0.074 0.021 3.533 0.000 0.162 0.162 RIsi5 0.221 0.028 7.860 0.000 0.537 0.537 RIsi6 0.122 0.026 4.614 0.000 0.305 0.305 RIadhd9 ~~
RIadhd10 0.297 0.021 14.318 0.000 0.627 0.627 RIadhd11 0.207 0.021 9.887 0.000 0.449 0.449 RIadhd12 0.112 0.021 5.309 0.000 0.253 0.253 RIadhd13 0.176 0.022 8.033 0.000 0.383 0.383 RIadhd14 0.271 0.023 11.999 0.000 0.542 0.542 RIadhd15 0.270 0.023 11.546 0.000 0.528 0.528 RIadhd16 0.316 0.024 13.268 0.000 0.634 0.634 RIadhd17 0.204 0.021 9.719 0.000 0.394 0.394 RIadhd18 0.138 0.020 7.043 0.000 0.260 0.260 RIsi1 0.178 0.030 6.005 0.000 0.513 0.513 RIsi2 0.238 0.030 7.992 0.000 0.625 0.625 RIsi3 0.143 0.029 4.872 0.000 0.439 0.439 RIsi4 0.154 0.023 6.619 0.000 0.325 0.325 RIsi5 0.263 0.033 8.065 0.000 0.607 0.607 RIsi6 0.272 0.029 9.283 0.000 0.649 0.649 RIadhd10 ~~
RIadhd11 0.335 0.020 17.024 0.000 0.730 0.730 RIadhd12 0.222 0.020 10.967 0.000 0.501 0.501 RIadhd13 0.295 0.020 14.685 0.000 0.646 0.646 RIadhd14 0.331 0.021 15.885 0.000 0.664 0.664 RIadhd15 0.357 0.021 16.859 0.000 0.702 0.702 RIadhd16 0.337 0.022 15.264 0.000 0.680 0.680 RIadhd17 0.348 0.018 18.949 0.000 0.673 0.673 RIadhd18 0.251 0.018 14.060 0.000 0.472 0.472 RIsi1 0.152 0.028 5.448 0.000 0.440 0.440 RIsi2 0.289 0.028 10.215 0.000 0.761 0.761 RIsi3 0.218 0.028 7.691 0.000 0.674 0.674 RIsi4 0.036 0.023 1.574 0.115 0.076 0.076 RIsi5 0.338 0.030 11.181 0.000 0.784 0.784 RIsi6 0.193 0.029 6.681 0.000 0.463 0.463 RIadhd11 ~~
RIadhd12 0.286 0.019 14.708 0.000 0.667 0.667 RIadhd13 0.358 0.019 18.793 0.000 0.808 0.808 RIadhd14 0.263 0.021 12.764 0.000 0.544 0.544 RIadhd15 0.300 0.021 14.387 0.000 0.608 0.608 RIadhd16 0.271 0.022 12.313 0.000 0.564 0.564 RIadhd17 0.310 0.018 17.112 0.000 0.619 0.619 RIadhd18 0.338 0.017 20.355 0.000 0.656 0.656 RIsi1 0.152 0.027 5.585 0.000 0.451 0.451 RIsi2 0.232 0.028 8.323 0.000 0.632 0.632 RIsi3 0.206 0.028 7.425 0.000 0.656 0.656 RIsi4 0.036 0.022 1.673 0.094 0.079 0.079 RIsi5 0.302 0.029 10.286 0.000 0.721 0.721 RIsi6 0.147 0.028 5.261 0.000 0.364 0.364 RIadhd12 ~~
RIadhd13 0.366 0.019 18.942 0.000 0.856 0.856 RIadhd14 0.170 0.021 8.083 0.000 0.366 0.366 RIadhd15 0.209 0.021 9.782 0.000 0.438 0.438 RIadhd16 0.172 0.022 7.731 0.000 0.371 0.371 RIadhd17 0.314 0.018 17.388 0.000 0.651 0.651 RIadhd18 0.336 0.016 20.655 0.000 0.676 0.676 RIsi1 0.115 0.027 4.269 0.000 0.355 0.355 RIsi2 0.156 0.029 5.430 0.000 0.439 0.439 RIsi3 0.154 0.028 5.510 0.000 0.508 0.508 RIsi4 0.015 0.022 0.700 0.484 0.035 0.035 RIsi5 0.199 0.030 6.645 0.000 0.494 0.494 RIsi6 0.043 0.028 1.526 0.127 0.111 0.111 RIadhd13 ~~
RIadhd14 0.254 0.021 12.092 0.000 0.527 0.527 RIadhd15 0.317 0.021 14.889 0.000 0.644 0.644 RIadhd16 0.313 0.023 13.784 0.000 0.652 0.652 RIadhd17 0.347 0.018 19.203 0.000 0.695 0.695 RIadhd18 0.293 0.017 17.556 0.000 0.569 0.569 RIsi1 0.155 0.027 5.720 0.000 0.461 0.461 RIsi2 0.276 0.028 9.836 0.000 0.754 0.754 RIsi3 0.219 0.028 7.916 0.000 0.700 0.700 RIsi4 0.040 0.022 1.807 0.071 0.087 0.087 RIsi5 0.305 0.030 10.222 0.000 0.731 0.731 RIsi6 0.152 0.028 5.391 0.000 0.377 0.377 RIadhd14 ~~
RIadhd15 0.535 0.021 25.223 0.000 0.997 0.997 RIadhd16 0.417 0.023 18.255 0.000 0.798 0.798 RIadhd17 0.394 0.019 21.084 0.000 0.724 0.724 RIadhd18 0.228 0.019 12.278 0.000 0.407 0.407 RIsi1 0.147 0.029 5.078 0.000 0.403 0.403 RIsi2 0.242 0.030 8.067 0.000 0.605 0.605 RIsi3 0.153 0.029 5.239 0.000 0.449 0.449 RIsi4 0.075 0.023 3.213 0.001 0.151 0.151 RIsi5 0.265 0.031 8.490 0.000 0.582 0.582 RIsi6 0.201 0.029 6.826 0.000 0.457 0.457 RIadhd15 ~~
RIadhd16 0.526 0.023 22.814 0.000 0.986 0.986 RIadhd17 0.487 0.018 26.457 0.000 0.876 0.876 RIadhd18 0.265 0.019 13.886 0.000 0.463 0.463 RIsi1 0.146 0.030 4.906 0.000 0.390 0.390 RIsi2 0.276 0.031 8.987 0.000 0.676 0.676 RIsi3 0.159 0.030 5.265 0.000 0.455 0.455 RIsi4 0.087 0.024 3.635 0.000 0.170 0.170 RIsi5 0.297 0.032 9.197 0.000 0.640 0.640 RIsi6 0.219 0.030 7.244 0.000 0.487 0.487 RIadhd16 ~~
RIadhd17 0.450 0.020 22.495 0.000 0.830 0.830 RIadhd18 0.226 0.020 11.120 0.000 0.406 0.406 RIsi1 0.175 0.031 5.696 0.000 0.481 0.481 RIsi2 0.257 0.032 8.128 0.000 0.645 0.645 RIsi3 0.174 0.031 5.624 0.000 0.511 0.511 RIsi4 0.090 0.025 3.610 0.000 0.181 0.181 RIsi5 0.283 0.033 8.509 0.000 0.625 0.625 RIsi6 0.228 0.031 7.322 0.000 0.520 0.520 RIadhd17 ~~
RIadhd18 0.334 0.017 19.881 0.000 0.575 0.575 RIsi1 0.102 0.025 4.122 0.000 0.269 0.269 RIsi2 0.204 0.025 8.176 0.000 0.492 0.492 RIsi3 0.157 0.024 6.557 0.000 0.444 0.444 RIsi4 0.028 0.022 1.308 0.191 0.055 0.055 RIsi5 0.230 0.028 8.306 0.000 0.489 0.489 RIsi6 0.116 0.027 4.329 0.000 0.254 0.254 RIadhd18 ~~
RIsi1 0.120 0.023 5.303 0.000 0.307 0.307 RIsi2 0.121 0.023 5.304 0.000 0.284 0.284 RIsi3 0.155 0.021 7.414 0.000 0.426 0.426 RIsi4 -0.023 0.020 -1.179 0.238 -0.044 -0.044 RIsi5 0.167 0.026 6.418 0.000 0.345 0.345 RIsi6 -0.025 0.025 -1.026 0.305 -0.054 -0.054 RIsi1 ~~
RIsi2 0.095 0.066 1.426 0.154 0.340 0.340 RIsi3 0.111 0.066 1.685 0.092 0.466 0.466 RIsi4 0.093 0.046 2.017 0.044 0.267 0.267 RIsi5 0.107 0.070 1.529 0.126 0.337 0.337 RIsi6 0.133 0.062 2.141 0.032 0.434 0.434 RIsi2 ~~
RIsi3 0.011 0.069 0.156 0.876 0.041 0.041 RIsi4 0.180 0.048 3.725 0.000 0.474 0.474 RIsi5 0.343 0.075 4.549 0.000 0.990 0.990 RIsi6 0.214 0.067 3.190 0.001 0.638 0.638 RIsi3 ~~
RIsi4 -0.056 0.046 -1.210 0.226 -0.172 -0.172 RIsi5 0.020 0.073 0.272 0.785 0.067 0.067 RIsi6 -0.007 0.063 -0.116 0.907 -0.026 -0.026 RIsi4 ~~
RIsi5 0.150 0.052 2.883 0.004 0.348 0.348 RIsi6 0.346 0.047 7.373 0.000 0.828 0.828 RIsi5 ~~
RIsi6 0.210 0.071 2.961 0.003 0.551 0.551

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.244 0.244 0.244 .pe81m7 0.202 0.202 0.202 .pe81m10 0.181 0.181 0.181 .pe81m12 0.120 0.120 0.120 .pe82m5 0.329 0.329 0.329 .pe82m7 0.285 0.285 0.285 .pe82m10 0.264 0.264 0.264 .pe82m12 0.200 0.200 0.200 .pe83m5 0.290 0.290 0.290 .pe83m7 0.245 0.245 0.245 .pe83m10 0.224 0.224 0.224 .pe83m12 0.159 0.159 0.159 .pe86m5 0.361 0.361 0.361 .pe86m7 0.327 0.327 0.327 .pe86m10 0.311 0.311 0.311 .pe86m12 0.261 0.261 0.261 .pe87m5 0.408 0.408 0.408 .pe87m7 0.382 0.382 0.382 .pe87m10 0.370 0.370 0.370 .pe87m12 0.332 0.332 0.332 .pe88m5 0.449 0.449 0.449 .pe88m7 0.422 0.422 0.422 .pe88m10 0.409 0.409 0.409 .pe88m12 0.369 0.369 0.369 .pe89m5 0.418 0.418 0.418 .pe89m7 0.393 0.393 0.393 .pe89m10 0.381 0.381 0.381 .pe89m12 0.345 0.345 0.345 .pe90m5 0.438 0.438 0.438 .pe90m7 0.416 0.416 0.416 .pe90m10 0.405 0.405 0.405 .pe90m12 0.373 0.373 0.373 .pe91m5 0.349 0.349 0.349 .pe91m7 0.319 0.319 0.319 .pe91m10 0.304 0.304 0.304 .pe91m12 0.260 0.260 0.260 .pe84m5 0.336 0.336 0.336 .pe84m7 0.303 0.303 0.303 .pe84m10 0.287 0.287 0.287 .pe84m12 0.239 0.239 0.239 .pe85m5 0.371 0.371 0.371 .pe85m7 0.340 0.340 0.340 .pe85m10 0.325 0.325 0.325 .pe85m12 0.278 0.278 0.278 .pe96m5 0.394 0.394 0.394 .pe96m7 0.361 0.361 0.361 .pe96m10 0.345 0.345 0.345 .pe96m12 0.296 0.296 0.296 .pe97m5 0.369 0.369 0.369 .pe97m7 0.337 0.337 0.337 .pe97m10 0.321 0.321 0.321 .pe97m12 0.274 0.274 0.274 .pe92m5 0.271 0.271 0.271 .pe92m7 0.237 0.237 0.237 .pe92m10 0.220 0.220 0.220 .pe92m12 0.169 0.169 0.169 .pe93m5 0.243 0.243 0.243 .pe93m7 0.207 0.207 0.207 .pe93m10 0.190 0.190 0.190 .pe93m12 0.138 0.138 0.138 .pe94m5 0.259 0.259 0.259 .pe94m7 0.221 0.221 0.221 .pe94m10 0.203 0.203 0.203 .pe94m12 0.147 0.147 0.147 .pe95m5 0.336 0.336 0.336 .pe95m7 0.319 0.319 0.319 .pe95m10 0.311 0.311 0.311 .pe95m12 0.286 0.286 0.286 .pe64m5 0.342 0.342 0.342 .pe64m7 0.332 0.332 0.332 .pe64m10 0.327 0.327 0.327 .pe64m12 0.312 0.312 0.312 .pe2m5 0.435 0.435 0.435 .pe2m7 0.358 0.358 0.358 .pe2m10 0.365 0.365 0.365 .pe2m12 0.320 0.320 0.320 .pe4m5 0.321 0.321 0.321 .pe4m7 0.229 0.229 0.229 .pe4m10 0.237 0.237 0.237 .pe4m12 0.184 0.184 0.184 .pe7m5 0.399 0.399 0.399 .pe7m7 0.305 0.305 0.305 .pe7m10 0.313 0.313 0.313 .pe7m12 0.259 0.259 0.259 .pe11m5 0.380 0.380 0.380 .pe11m7 0.344 0.344 0.344 .pe11m10 0.347 0.347 0.347 .pe11m12 0.326 0.326 0.326 .pe13m5 0.237 0.237 0.237 .pe13m7 0.146 0.146 0.146 .pe13m10 0.154 0.154 0.154 .pe13m12 0.102 0.102 0.102 .pe25m5 0.319 0.319 0.319 .pe25m7 0.241 0.241 0.241 .pe25m10 0.249 0.249 0.249 .pe25m12 0.204 0.204 0.204 RIadhd1 0.511 0.024 21.641 0.000 1.000 1.000 RIadhd2 0.415 0.025 16.808 0.000 1.000 1.000 RIadhd3 0.452 0.024 18.607 0.000 1.000 1.000 RIadhd4 0.440 0.022 20.126 0.000 1.000 1.000 RIadhd5 0.443 0.020 21.983 0.000 1.000 1.000 RIadhd6 0.393 0.021 18.860 0.000 1.000 1.000 RIadhd7 0.438 0.021 20.953 0.000 1.000 1.000 RIadhd8 0.432 0.019 22.947 0.000 1.000 1.000 RIadhd9 0.476 0.023 20.438 0.000 1.000 1.000 RIadhd10 0.472 0.021 22.719 0.000 1.000 1.000 RIadhd11 0.445 0.020 22.134 0.000 1.000 1.000 RIadhd12 0.413 0.021 20.125 0.000 1.000 1.000 RIadhd13 0.442 0.021 21.220 0.000 1.000 1.000 RIadhd14 0.525 0.022 24.303 0.000 1.000 1.000 RIadhd15 0.548 0.023 24.043 0.000 1.000 1.000 RIadhd16 0.520 0.026 20.279 0.000 1.000 1.000 RIadhd17 0.564 0.017 33.343 0.000 1.000 1.000 RIadhd18 0.598 0.014 43.716 0.000 1.000 1.000 RIsi1 0.254 0.068 3.769 0.000 1.000 1.000 RIsi2 0.304 0.074 4.089 0.000 1.000 1.000 RIsi3 0.222 0.072 3.085 0.002 1.000 1.000 RIsi4 0.475 0.037 12.852 0.000 1.000 1.000 RIsi5 0.394 0.080 4.947 0.000 1.000 1.000 RIsi6 0.368 0.067 5.468 0.000 1.000 1.000 WFadhd5 0.245 0.023 10.773 0.000 1.000 1.000 .WFadhd7 0.245 0.015 15.882 0.000 0.851 0.851 .WFadhd10 0.251 0.016 16.049 0.000 0.814 0.814 .WFadhd12 0.190 0.013 14.774 0.000 0.515 0.515 WFsi5 0.311 0.071 4.354 0.000 1.000 1.000 .WFsi7 0.246 0.038 6.520 0.000 0.636 0.636 .WFsi10 0.240 0.036 6.608 0.000 0.631 0.631 .WFsi12 0.173 0.029 5.990 0.000 0.408 0.408

S2 Model fit: Comparative Fit Index (CFI) 0.984 (>0.95) Change in CFI: 0.002 (decrease) - worse fit Tucker-Lewis Index (TLI) 0.983 (>0.95) Change in TLI: 0.001 (decrease) - worse fit
RMSEA 0.017 (≤ 0.06) Change in RMSEA: 0.001 (increase) - worse fit
90 Percent confidence interval - lower 0.016 90 Percent confidence interval - upper 0.018
SRMR 0.038 (≤ 0.08) Change in SRMR: 0.005 (increase) - worse fit

summary(semTools::compareFit(RICLPM_multi_adhd_S1.fit, RICLPM_multi_adhd_S2.fit, nested = TRUE)) #† indicates the best fitting model

Nested Model Comparison

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

                       Df AIC BIC  Chisq Chisq diff Df diff Pr(>Chisq)

RICLPM_multi_adhd_S1.fit 4148 5796.9
RICLPM_multi_adhd_S2.fit 4214 8153.1 304.38 66 < 2.2e-16 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Model Fit Indices

                     chisq.scaled df.scaled pvalue.scaled rmsea.scaled

RICLPM_multi_adhd_S1.fit 6406.447† 4148 .000 .016† RICLPM_multi_adhd_S2.fit 6806.703 4214 .000 .017 cfi.scaled tli.scaled srmr RICLPM_multi_adhd_S1.fit .986† .985† .032† RICLPM_multi_adhd_S2.fit .984 .983 .037

Differences in Fit Indices

                                                df.scaled rmsea.scaled

RICLPM_multi_adhd_S2.fit - RICLPM_multi_adhd_S1.fit 66 0.001 cfi.scaled tli.scaled srmr RICLPM_multi_adhd_S2.fit - RICLPM_multi_adhd_S1.fit -0.002 -0.002 0.005

Significant;y worse fit according to the chi square (p<0.0001).

However, since the change in fit did not exceed 0.01 for all indices - we can except that strong invariance holds.

RICLPM_multi_adhd_S3: ADHD step 3

Multiple response items RICLPM mother report ADHD symptoms and social isolation: Step 3

Fitting a model with constraints to ensure strong factorial invariance, with a random intercept for each indicator separately.

RICLPM_multi_adhd_S3 <- '
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of ADHD (mother report)
  # Inattention symptoms
  RIadhd1 =~ 1*pe81m5 + 1*pe81m7 + 1*pe81m10 + 1*pe81m12
  RIadhd2 =~ 1*pe82m5 + 1*pe82m7 + 1*pe82m10 + 1*pe82m12
  RIadhd3 =~ 1*pe83m5 + 1*pe83m7 + 1*pe83m10 + 1*pe83m12
  RIadhd4 =~ 1*pe86m5 + 1*pe86m7 + 1*pe86m10 + 1*pe86m12
  RIadhd5 =~ 1*pe87m5 + 1*pe87m7 + 1*pe87m10 + 1*pe87m12
  RIadhd6 =~ 1*pe88m5 + 1*pe88m7 + 1*pe88m10 + 1*pe88m12
  RIadhd7 =~ 1*pe89m5 + 1*pe89m7 + 1*pe89m10 + 1*pe89m12
  RIadhd8 =~ 1*pe90m5 + 1*pe90m7 + 1*pe90m10 + 1*pe90m12
  RIadhd9 =~ 1*pe91m5 + 1*pe91m7 + 1*pe91m10 + 1*pe91m12
  #Hyperactivity symptoms
  RIadhd10 =~ 1*pe84m5 + 1*pe84m7 + 1*pe84m10 + 1*pe84m12
  RIadhd11 =~ 1*pe85m5 + 1*pe85m7 + 1*pe85m10 + 1*pe85m12
  RIadhd12 =~ 1*pe96m5 + 1*pe96m7 + 1*pe96m10 + 1*pe96m12
  RIadhd13 =~ 1*pe97m5 + 1*pe97m7 + 1*pe97m10 + 1*pe97m12
  RIadhd14 =~ 1*pe92m5 + 1*pe92m7 + 1*pe92m10 + 1*pe92m12
  RIadhd15 =~ 1*pe93m5 + 1*pe93m7 + 1*pe93m10 + 1*pe93m12
  RIadhd16 =~ 1*pe94m5 + 1*pe94m7 + 1*pe94m10 + 1*pe94m12
  RIadhd17 =~ 1*pe95m5 + 1*pe95m7 + 1*pe95m10 + 1*pe95m12
  RIadhd18 =~ 1*pe64m5 + 1*pe64m7 + 1*pe64m10 + 1*pe64m12
  
  # Create between factors (random intercepts) for each item of social isolation (mother report)
  RIsi1 =~ 1*pe2m5 + 1*pe2m7 + 1*pe2m10 + 1*pe2m12 
  RIsi2 =~ 1*pe4m5 + 1*pe4m7 + 1*pe4m10 + 1*pe4m12
  RIsi3 =~ 1*pe7m5 + 1*pe7m7 + 1*pe7m10 + 1*pe7m12
  RIsi4 =~ 1*pe11m5 + 1*pe11m7 + 1*pe11m10 + 1*pe11m12
  RIsi5 =~ 1*pe13m5 + 1*pe13m7 + 1*pe13m10 + 1*pe13m12
  RIsi6 =~ 1*pe25m5 + 1*pe25m7 + 1*pe25m10 + 1*pe25m12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for ADHD (inattention and hyperactivity) symptoms at 4 waves
  WFadhd5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5 + j*pe84m5 + k*pe85m5 + l*pe96m5 + m*pe97m5 + n*pe92m5 + o*pe93m5 + p*pe94m5 + q*pe95m5 + r*pe64m5
  WFadhd7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7 + j*pe84m7 + k*pe85m7 + l*pe96m7 + m*pe97m7 + n*pe92m7 + o*pe93m7 + p*pe94m7 + q*pe95m7 + r*pe64m7
  WFadhd10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10 + j*pe84m10 + k*pe85m10 + l*pe96m10 + m*pe97m10 + n*pe92m10 + o*pe93m10 + p*pe94m10 + q*pe95m10 + r*pe64m10
  WFadhd12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 + j*pe84m12 + k*pe85m12 + l*pe96m12 + m*pe97m12 + n*pe92m12 + o*pe93m12 + p*pe94m12 + q*pe95m12 + r*pe64m12 
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ s*pe2m5 + t*pe4m5 + u*pe7m5 + v*pe11m5 + w*pe13m5 + x*pe25m5 
  WFsi7 =~ s*pe2m7 + t*pe4m7 + u*pe7m7 + v*pe11m7 + w*pe13m7 + x*pe25m7 
  WFsi10 =~ s*pe2m10 + t*pe4m10 + u*pe7m10 + v*pe11m10 + w*pe13m10 + x*pe25m10
  WFsi12 =~ s*pe2m12 + t*pe4m12 + u*pe7m12 + v*pe11m12 + w*pe13m12 + x*pe25m12
  
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  # Inattention symptoms
  pe81m5 + pe81m7 + pe81m10 + pe81m12 ~ y*1
  pe82m5 + pe82m7 + pe82m10 + pe82m12 ~ z*1
  pe83m5 + pe83m7 + pe83m10 + pe83m12 ~ aa*1
  pe86m5 + pe86m7 + pe86m10 + pe86m12 ~ ab*1
  pe87m5 + pe87m7 + pe87m10 + pe87m12 ~ ac*1
  pe88m5 + pe88m7 + pe88m10 + pe88m12 ~ ad*1
  pe89m5 + pe89m7 + pe89m10 + pe89m12 ~ ae*1
  pe90m5 + pe90m7 + pe90m10 + pe90m12 ~ af*1
  pe91m5 + pe91m7 + pe91m10 + pe91m12 ~ ag*1
  # Hyperactivity symptoms
  pe84m5 + pe84m7 + pe84m10 + pe84m12 ~ ah*1
  pe85m5 + pe85m7 + pe85m10 + pe85m12 ~ ai*1
  pe96m5 + pe96m7 + pe96m10 + pe96m12 ~ aj*1
  pe97m5 + pe97m7 + pe97m10 + pe97m12 ~ ak*1
  pe92m5 + pe92m7 + pe92m10 + pe92m12 ~ al*1
  pe93m5 + pe93m7 + pe93m10 + pe93m12 ~ am*1
  pe94m5 + pe94m7 + pe94m10 + pe94m12 ~ an*1
  pe95m5 + pe95m7 + pe95m10 + pe95m12 ~ ao*1
  pe64m5 + pe64m7 + pe64m10 + pe64m12 ~ ap*1

  # Social isolation
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ aq*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ ar*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ as*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ at*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ au*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ av*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  WFadhd7 + WFadhd10 + WFadhd12 + WFsi7 + WFsi10 + WFsi12 ~ 1
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFadhd7 + WFsi7 ~ WFadhd5 + WFsi5
  WFadhd10 + WFsi10 ~ WFadhd7 + WFsi7
  WFadhd12 + WFsi12 ~ WFadhd10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFadhd5 ~~ WFsi5
  WFadhd7 ~~ WFsi7
  WFadhd10 ~~ WFsi10 
  WFadhd12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIadhd1 + RIadhd2 + RIadhd3 + RIadhd4 + RIadhd5 + RIadhd6 + RIadhd7 + RIadhd8 + RIadhd9 + RIadhd10 + RIadhd11 + RIadhd12 + RIadhd13 + RIadhd14 + RIadhd15 + RIadhd16 + RIadhd17 + RIadhd18 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFadhd5
'

RICLPM_multi_adhd_S3.fit <- cfa(RICLPM_multi_adhd_S3, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

RICLPM_multi_adhd_S3.fit.summary <- summary(RICLPM_multi_adhd_S3.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 280 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 706 Number of equality constraints 138

Number of observations 2232 Number of missing patterns 70

Model Test User Model: Standard Robust Test Statistic 8153.127 6767.457 Degrees of freedom 4184 4184 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.037 Shift parameter 2765.369 simple second-order correction

Model Test Baseline Model:

Test statistic 953296.763 166852.672 Degrees of freedom 4560 4560 P-value 0.000 0.000 Scaling correction factor 5.846

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.996 0.984 Tucker-Lewis Index (TLI) 0.995 0.983

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.021 0.017 90 Percent confidence interval - lower 0.020 0.016 90 Percent confidence interval - upper 0.021 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.037 0.037

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIadhd1 =~
pe81m5 1.000 0.715 0.715 pe81m7 1.000 0.715 0.715 pe81m10 1.000 0.715 0.715 pe81m12 1.000 0.715 0.715 RIadhd2 =~
pe82m5 1.000 0.644 0.644 pe82m7 1.000 0.644 0.644 pe82m10 1.000 0.644 0.644 pe82m12 1.000 0.644 0.644 RIadhd3 =~
pe83m5 1.000 0.672 0.672 pe83m7 1.000 0.672 0.672 pe83m10 1.000 0.672 0.672 pe83m12 1.000 0.672 0.672 RIadhd4 =~
pe86m5 1.000 0.663 0.663 pe86m7 1.000 0.663 0.663 pe86m10 1.000 0.663 0.663 pe86m12 1.000 0.663 0.663 RIadhd5 =~
pe87m5 1.000 0.666 0.666 pe87m7 1.000 0.666 0.666 pe87m10 1.000 0.666 0.666 pe87m12 1.000 0.666 0.666 RIadhd6 =~
pe88m5 1.000 0.627 0.627 pe88m7 1.000 0.627 0.627 pe88m10 1.000 0.627 0.627 pe88m12 1.000 0.627 0.627 RIadhd7 =~
pe89m5 1.000 0.662 0.662 pe89m7 1.000 0.662 0.662 pe89m10 1.000 0.662 0.662 pe89m12 1.000 0.662 0.662 RIadhd8 =~
pe90m5 1.000 0.657 0.657 pe90m7 1.000 0.657 0.657 pe90m10 1.000 0.657 0.657 pe90m12 1.000 0.657 0.657 RIadhd9 =~
pe91m5 1.000 0.690 0.690 pe91m7 1.000 0.690 0.690 pe91m10 1.000 0.690 0.690 pe91m12 1.000 0.690 0.690 RIadhd10 =~
pe84m5 1.000 0.687 0.687 pe84m7 1.000 0.687 0.687 pe84m10 1.000 0.687 0.687 pe84m12 1.000 0.687 0.687 RIadhd11 =~
pe85m5 1.000 0.667 0.667 pe85m7 1.000 0.667 0.667 pe85m10 1.000 0.667 0.667 pe85m12 1.000 0.667 0.667 RIadhd12 =~
pe96m5 1.000 0.643 0.643 pe96m7 1.000 0.643 0.643 pe96m10 1.000 0.643 0.643 pe96m12 1.000 0.643 0.643 RIadhd13 =~
pe97m5 1.000 0.665 0.665 pe97m7 1.000 0.665 0.665 pe97m10 1.000 0.665 0.665 pe97m12 1.000 0.665 0.665 RIadhd14 =~
pe92m5 1.000 0.725 0.725 pe92m7 1.000 0.725 0.725 pe92m10 1.000 0.725 0.725 pe92m12 1.000 0.725 0.725 RIadhd15 =~
pe93m5 1.000 0.740 0.740 pe93m7 1.000 0.740 0.740 pe93m10 1.000 0.740 0.740 pe93m12 1.000 0.740 0.740 RIadhd16 =~
pe94m5 1.000 0.721 0.721 pe94m7 1.000 0.721 0.721 pe94m10 1.000 0.721 0.721 pe94m12 1.000 0.721 0.721 RIadhd17 =~
pe95m5 1.000 0.751 0.751 pe95m7 1.000 0.751 0.751 pe95m10 1.000 0.751 0.751 pe95m12 1.000 0.751 0.751 RIadhd18 =~
pe64m5 1.000 0.773 0.773 pe64m7 1.000 0.773 0.773 pe64m10 1.000 0.773 0.773 pe64m12 1.000 0.773 0.773 RIsi1 =~
pe2m5 1.000 0.504 0.504 pe2m7 1.000 0.504 0.504 pe2m10 1.000 0.504 0.504 pe2m12 1.000 0.504 0.504 RIsi2 =~
pe4m5 1.000 0.552 0.552 pe4m7 1.000 0.552 0.552 pe4m10 1.000 0.552 0.552 pe4m12 1.000 0.552 0.552 RIsi3 =~
pe7m5 1.000 0.471 0.471 pe7m7 1.000 0.471 0.471 pe7m10 1.000 0.471 0.471 pe7m12 1.000 0.471 0.471 RIsi4 =~
pe11m5 1.000 0.689 0.689 pe11m7 1.000 0.689 0.689 pe11m10 1.000 0.689 0.689 pe11m12 1.000 0.689 0.689 RIsi5 =~
pe13m5 1.000 0.627 0.627 pe13m7 1.000 0.627 0.627 pe13m10 1.000 0.627 0.627 pe13m12 1.000 0.627 0.627 RIsi6 =~
pe25m5 1.000 0.607 0.607 pe25m7 1.000 0.607 0.607 pe25m10 1.000 0.607 0.607 pe25m12 1.000 0.607 0.607 WFadhd5 =~
pe81m5 (a) 1.000 0.495 0.495 pe82m5 (b) 1.021 0.030 34.200 0.000 0.506 0.506 pe83m5 (c) 1.026 0.026 39.105 0.000 0.508 0.508 pe86m5 (d) 0.901 0.032 28.174 0.000 0.446 0.446 pe87m5 (e) 0.780 0.038 20.757 0.000 0.387 0.387 pe88m5 (f) 0.803 0.038 21.214 0.000 0.398 0.398 pe89m5 (g) 0.767 0.037 20.981 0.000 0.380 0.380 pe90m5 (h) 0.727 0.038 18.967 0.000 0.360 0.360 pe91m5 (i) 0.845 0.039 21.832 0.000 0.419 0.419 pe84m5 (j) 0.884 0.034 25.989 0.000 0.438 0.438 pe85m5 (k) 0.866 0.035 24.479 0.000 0.429 0.429 pe96m5 (l) 0.887 0.041 21.883 0.000 0.439 0.439 pe97m5 (m) 0.878 0.036 24.587 0.000 0.435 0.435 pe92m5 (n) 0.910 0.033 27.443 0.000 0.451 0.451 pe93m5 (o) 0.923 0.030 30.317 0.000 0.457 0.457 pe94m5 (p) 0.949 0.035 27.145 0.000 0.470 0.470 pe95m5 (q) 0.637 0.035 18.171 0.000 0.316 0.316 pe64m5 (r) 0.496 0.037 13.386 0.000 0.246 0.246 WFadhd7 =~
pe81m7 (a) 1.000 0.536 0.536 pe82m7 (b) 1.021 0.030 34.200 0.000 0.548 0.548 pe83m7 (c) 1.026 0.026 39.105 0.000 0.550 0.550 pe86m7 (d) 0.901 0.032 28.174 0.000 0.483 0.483 pe87m7 (e) 0.780 0.038 20.757 0.000 0.418 0.418 pe88m7 (f) 0.803 0.038 21.214 0.000 0.430 0.430 pe89m7 (g) 0.767 0.037 20.981 0.000 0.411 0.411 pe90m7 (h) 0.727 0.038 18.967 0.000 0.390 0.390 pe91m7 (i) 0.845 0.039 21.832 0.000 0.453 0.453 pe84m7 (j) 0.884 0.034 25.989 0.000 0.474 0.474 pe85m7 (k) 0.866 0.035 24.479 0.000 0.464 0.464 pe96m7 (l) 0.887 0.041 21.883 0.000 0.475 0.475 pe97m7 (m) 0.878 0.036 24.587 0.000 0.471 0.471 pe92m7 (n) 0.910 0.033 27.443 0.000 0.488 0.488 pe93m7 (o) 0.923 0.030 30.317 0.000 0.495 0.495 pe94m7 (p) 0.949 0.035 27.145 0.000 0.509 0.509 pe95m7 (q) 0.637 0.035 18.171 0.000 0.342 0.342 pe64m7 (r) 0.496 0.037 13.386 0.000 0.266 0.266 WFadhd10 =~
pe81m10 (a) 1.000 0.555 0.555 pe82m10 (b) 1.021 0.030 34.200 0.000 0.567 0.567 pe83m10 (c) 1.026 0.026 39.105 0.000 0.569 0.569 pe86m10 (d) 0.901 0.032 28.174 0.000 0.500 0.500 pe87m10 (e) 0.780 0.038 20.757 0.000 0.433 0.433 pe88m10 (f) 0.803 0.038 21.214 0.000 0.445 0.445 pe89m10 (g) 0.767 0.037 20.981 0.000 0.425 0.425 pe90m10 (h) 0.727 0.038 18.967 0.000 0.403 0.403 pe91m10 (i) 0.845 0.039 21.832 0.000 0.469 0.469 pe84m10 (j) 0.884 0.034 25.989 0.000 0.490 0.490 pe85m10 (k) 0.866 0.035 24.479 0.000 0.480 0.480 pe96m10 (l) 0.887 0.041 21.883 0.000 0.492 0.492 pe97m10 (m) 0.878 0.036 24.587 0.000 0.487 0.487 pe92m10 (n) 0.910 0.033 27.443 0.000 0.505 0.505 pe93m10 (o) 0.923 0.030 30.317 0.000 0.512 0.512 pe94m10 (p) 0.949 0.035 27.145 0.000 0.526 0.526 pe95m10 (q) 0.637 0.035 18.171 0.000 0.354 0.354 pe64m10 (r) 0.496 0.037 13.386 0.000 0.275 0.275 WFadhd12 =~
pe81m12 (a) 1.000 0.608 0.608 pe82m12 (b) 1.021 0.030 34.200 0.000 0.621 0.621 pe83m12 (c) 1.026 0.026 39.105 0.000 0.624 0.624 pe86m12 (d) 0.901 0.032 28.174 0.000 0.547 0.547 pe87m12 (e) 0.780 0.038 20.757 0.000 0.474 0.474 pe88m12 (f) 0.803 0.038 21.214 0.000 0.488 0.488 pe89m12 (g) 0.767 0.037 20.981 0.000 0.466 0.466 pe90m12 (h) 0.727 0.038 18.967 0.000 0.442 0.442 pe91m12 (i) 0.845 0.039 21.832 0.000 0.514 0.514 pe84m12 (j) 0.884 0.034 25.989 0.000 0.537 0.537 pe85m12 (k) 0.866 0.035 24.479 0.000 0.526 0.526 pe96m12 (l) 0.887 0.041 21.883 0.000 0.539 0.539 pe97m12 (m) 0.878 0.036 24.587 0.000 0.533 0.533 pe92m12 (n) 0.910 0.033 27.443 0.000 0.553 0.553 pe93m12 (o) 0.923 0.030 30.317 0.000 0.561 0.561 pe94m12 (p) 0.949 0.035 27.145 0.000 0.577 0.577 pe95m12 (q) 0.637 0.035 18.171 0.000 0.387 0.387 pe64m12 (r) 0.496 0.037 13.386 0.000 0.301 0.301 WFsi5 =~
pe2m5 (s) 1.000 0.558 0.558 pe4m5 (t) 1.097 0.100 10.939 0.000 0.612 0.612 pe7m5 (u) 1.105 0.105 10.486 0.000 0.616 0.616 pe11m5 (v) 0.684 0.085 8.006 0.000 0.382 0.382 pe13m5 (w) 1.090 0.112 9.772 0.000 0.608 0.608 pe25m5 (x) 1.003 0.105 9.541 0.000 0.559 0.559 WFsi7 =~
pe2m7 (s) 1.000 0.623 0.623 pe4m7 (t) 1.097 0.100 10.939 0.000 0.683 0.683 pe7m7 (u) 1.105 0.105 10.486 0.000 0.688 0.688 pe11m7 (v) 0.684 0.085 8.006 0.000 0.426 0.426 pe13m7 (w) 1.090 0.112 9.772 0.000 0.679 0.679 pe25m7 (x) 1.003 0.105 9.541 0.000 0.625 0.625 WFsi10 =~
pe2m10 (s) 1.000 0.617 0.617 pe4m10 (t) 1.097 0.100 10.939 0.000 0.677 0.677 pe7m10 (u) 1.105 0.105 10.486 0.000 0.682 0.682 pe11m10 (v) 0.684 0.085 8.006 0.000 0.422 0.422 pe13m10 (w) 1.090 0.112 9.772 0.000 0.672 0.672 pe25m10 (x) 1.003 0.105 9.541 0.000 0.619 0.619 WFsi12 =~
pe2m12 (s) 1.000 0.652 0.652 pe4m12 (t) 1.097 0.100 10.939 0.000 0.715 0.715 pe7m12 (u) 1.105 0.105 10.486 0.000 0.720 0.720 pe11m12 (v) 0.684 0.085 8.006 0.000 0.446 0.446 pe13m12 (w) 1.090 0.112 9.772 0.000 0.710 0.710 pe25m12 (x) 1.003 0.105 9.541 0.000 0.654 0.654

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd7 ~
WFadhd5 0.420 0.057 7.364 0.000 0.388 0.388 WFsi5 -0.089 0.064 -1.402 0.161 -0.093 -0.093 WFsi7 ~
WFadhd5 -0.057 0.075 -0.759 0.448 -0.045 -0.045 WFsi5 0.679 0.075 9.001 0.000 0.608 0.608 WFadhd10 ~
WFadhd7 0.450 0.052 8.602 0.000 0.435 0.435 WFsi7 -0.020 0.061 -0.330 0.741 -0.023 -0.023 WFsi10 ~
WFadhd7 0.010 0.062 0.154 0.877 0.008 0.008 WFsi7 0.600 0.075 8.032 0.000 0.605 0.605 WFadhd12 ~
WFadhd10 0.775 0.031 24.949 0.000 0.707 0.707 WFsi10 -0.049 0.039 -1.284 0.199 -0.050 -0.050 WFsi12 ~
WFadhd10 0.084 0.047 1.794 0.073 0.071 0.071 WFsi10 0.791 0.046 17.310 0.000 0.748 0.748

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd5 ~~
WFsi5 0.038 0.027 1.375 0.169 0.136 0.136 .WFadhd7 ~~
.WFsi7 0.088 0.015 5.810 0.000 0.359 0.359 .WFadhd10 ~~
.WFsi10 0.071 0.013 5.260 0.000 0.289 0.289 .WFadhd12 ~~
.WFsi12 0.080 0.011 6.954 0.000 0.439 0.439 RIadhd1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd7 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd8 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd9 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd10 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd11 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd12 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd13 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd14 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd15 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd16 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd17 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd18 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 RIadhd1 ~~
RIadhd2 0.451 0.023 19.566 0.000 0.979 0.979 RIadhd3 0.499 0.023 22.007 0.000 1.039 1.039 RIadhd4 0.385 0.022 17.566 0.000 0.812 0.812 RIadhd5 0.244 0.022 11.358 0.000 0.514 0.514 RIadhd6 0.274 0.021 13.053 0.000 0.613 0.613 RIadhd7 0.393 0.020 19.266 0.000 0.832 0.832 RIadhd8 0.171 0.021 8.063 0.000 0.364 0.364 RIadhd9 0.325 0.023 14.227 0.000 0.659 0.659 RIadhd10 0.331 0.022 15.353 0.000 0.674 0.674 RIadhd11 0.230 0.022 10.605 0.000 0.482 0.482 RIadhd12 0.075 0.022 3.353 0.001 0.163 0.163 RIadhd13 0.221 0.022 9.909 0.000 0.466 0.466 RIadhd14 0.348 0.023 15.377 0.000 0.673 0.673 RIadhd15 0.411 0.023 17.774 0.000 0.777 0.777 RIadhd16 0.450 0.024 19.052 0.000 0.873 0.873 RIadhd17 0.309 0.021 15.064 0.000 0.576 0.576 RIadhd18 0.157 0.020 7.850 0.000 0.284 0.284 RIsi1 0.156 0.030 5.148 0.000 0.433 0.433 RIsi2 0.264 0.032 8.372 0.000 0.669 0.669 RIsi3 0.149 0.032 4.716 0.000 0.442 0.442 RIsi4 0.110 0.024 4.582 0.000 0.224 0.224 RIsi5 0.297 0.033 8.954 0.000 0.662 0.662 RIsi6 0.261 0.031 8.527 0.000 0.601 0.601 RIadhd2 ~~
RIadhd3 0.416 0.023 17.970 0.000 0.960 0.960 RIadhd4 0.365 0.022 16.524 0.000 0.856 0.856 RIadhd5 0.205 0.022 9.493 0.000 0.478 0.478 RIadhd6 0.275 0.021 13.248 0.000 0.681 0.681 RIadhd7 0.370 0.021 17.979 0.000 0.868 0.868 RIadhd8 0.189 0.020 9.289 0.000 0.447 0.447 RIadhd9 0.295 0.023 13.085 0.000 0.664 0.664 RIadhd10 0.291 0.022 13.486 0.000 0.657 0.657 RIadhd11 0.192 0.021 9.002 0.000 0.447 0.447 RIadhd12 0.050 0.021 2.313 0.021 0.120 0.120 RIadhd13 0.180 0.022 8.193 0.000 0.420 0.420 RIadhd14 0.260 0.023 11.407 0.000 0.558 0.558 RIadhd15 0.300 0.024 12.744 0.000 0.630 0.630 RIadhd16 0.369 0.024 15.414 0.000 0.795 0.795 RIadhd17 0.215 0.020 10.552 0.000 0.444 0.444 RIadhd18 0.098 0.019 5.057 0.000 0.197 0.197 RIsi1 0.126 0.030 4.166 0.000 0.387 0.387 RIsi2 0.224 0.032 7.025 0.000 0.631 0.631 RIsi3 0.129 0.032 4.076 0.000 0.426 0.426 RIsi4 0.093 0.024 3.819 0.000 0.209 0.209 RIsi5 0.244 0.034 7.188 0.000 0.603 0.603 RIsi6 0.223 0.032 7.056 0.000 0.571 0.571 RIadhd3 ~~
RIadhd4 0.367 0.022 16.681 0.000 0.824 0.824 RIadhd5 0.230 0.021 10.755 0.000 0.514 0.514 RIadhd6 0.261 0.021 12.577 0.000 0.620 0.620 RIadhd7 0.350 0.021 16.955 0.000 0.788 0.788 RIadhd8 0.180 0.021 8.631 0.000 0.407 0.407 RIadhd9 0.309 0.023 13.679 0.000 0.666 0.666 RIadhd10 0.357 0.021 16.718 0.000 0.773 0.773 RIadhd11 0.251 0.021 11.775 0.000 0.559 0.559 RIadhd12 0.114 0.022 5.216 0.000 0.265 0.265 RIadhd13 0.235 0.022 10.784 0.000 0.527 0.527 RIadhd14 0.332 0.023 14.697 0.000 0.681 0.681 RIadhd15 0.381 0.023 16.473 0.000 0.766 0.766 RIadhd16 0.422 0.024 17.890 0.000 0.871 0.871 RIadhd17 0.317 0.020 16.021 0.000 0.629 0.629 RIadhd18 0.194 0.019 10.335 0.000 0.374 0.374 RIsi1 0.141 0.030 4.651 0.000 0.415 0.415 RIsi2 0.232 0.031 7.451 0.000 0.625 0.625 RIsi3 0.142 0.032 4.497 0.000 0.448 0.448 RIsi4 0.084 0.024 3.546 0.000 0.182 0.182 RIsi5 0.269 0.033 8.161 0.000 0.637 0.637 RIsi6 0.233 0.030 7.701 0.000 0.571 0.571 RIadhd4 ~~
RIadhd5 0.260 0.020 12.793 0.000 0.590 0.590 RIadhd6 0.309 0.020 15.488 0.000 0.743 0.743 RIadhd7 0.372 0.020 18.920 0.000 0.848 0.848 RIadhd8 0.219 0.020 11.126 0.000 0.502 0.502 RIadhd9 0.310 0.022 14.407 0.000 0.678 0.678 RIadhd10 0.343 0.020 16.842 0.000 0.753 0.753 RIadhd11 0.268 0.020 13.372 0.000 0.606 0.606 RIadhd12 0.122 0.021 5.903 0.000 0.287 0.287 RIadhd13 0.229 0.021 11.009 0.000 0.520 0.520 RIadhd14 0.282 0.022 12.964 0.000 0.587 0.587 RIadhd15 0.321 0.022 14.403 0.000 0.655 0.655 RIadhd16 0.353 0.023 15.521 0.000 0.738 0.738 RIadhd17 0.238 0.020 12.154 0.000 0.477 0.477 RIadhd18 0.154 0.019 8.293 0.000 0.301 0.301 RIsi1 0.123 0.028 4.432 0.000 0.368 0.368 RIsi2 0.241 0.029 8.330 0.000 0.659 0.659 RIsi3 0.148 0.029 5.125 0.000 0.474 0.474 RIsi4 0.079 0.023 3.457 0.001 0.172 0.172 RIsi5 0.274 0.031 8.927 0.000 0.659 0.659 RIsi6 0.218 0.028 7.653 0.000 0.541 0.541 RIadhd5 ~~
RIadhd6 0.267 0.019 14.059 0.000 0.641 0.641 RIadhd7 0.238 0.020 12.160 0.000 0.541 0.541 RIadhd8 0.194 0.019 10.072 0.000 0.444 0.444 RIadhd9 0.319 0.021 15.131 0.000 0.696 0.696 RIadhd10 0.252 0.020 12.576 0.000 0.551 0.551 RIadhd11 0.221 0.019 11.499 0.000 0.497 0.497 RIadhd12 0.101 0.020 5.155 0.000 0.236 0.236 RIadhd13 0.185 0.020 9.292 0.000 0.419 0.419 RIadhd14 0.219 0.021 10.455 0.000 0.455 0.455 RIadhd15 0.236 0.022 10.887 0.000 0.478 0.478 RIadhd16 0.240 0.022 10.757 0.000 0.500 0.500 RIadhd17 0.174 0.019 8.951 0.000 0.348 0.348 RIadhd18 0.114 0.018 6.206 0.000 0.221 0.221 RIsi1 0.182 0.026 6.918 0.000 0.543 0.543 RIsi2 0.263 0.027 9.752 0.000 0.716 0.716 RIsi3 0.132 0.026 4.991 0.000 0.420 0.420 RIsi4 0.202 0.022 9.220 0.000 0.441 0.441 RIsi5 0.302 0.029 10.348 0.000 0.724 0.724 RIsi6 0.279 0.027 10.427 0.000 0.692 0.692 RIadhd6 ~~
RIadhd7 0.291 0.019 15.239 0.000 0.701 0.701 RIadhd8 0.346 0.018 18.768 0.000 0.840 0.840 RIadhd9 0.325 0.021 15.654 0.000 0.753 0.753 RIadhd10 0.266 0.019 13.690 0.000 0.617 0.617 RIadhd11 0.188 0.019 9.890 0.000 0.451 0.451 RIadhd12 0.066 0.019 3.468 0.001 0.164 0.164 RIadhd13 0.157 0.020 8.012 0.000 0.378 0.378 RIadhd14 0.212 0.021 10.204 0.000 0.468 0.468 RIadhd15 0.225 0.021 10.594 0.000 0.486 0.486 RIadhd16 0.242 0.022 10.945 0.000 0.535 0.535 RIadhd17 0.146 0.018 7.896 0.000 0.309 0.309 RIadhd18 0.100 0.017 5.790 0.000 0.207 0.207 RIsi1 0.096 0.027 3.568 0.000 0.303 0.303 RIsi2 0.222 0.026 8.417 0.000 0.643 0.643 RIsi3 0.115 0.027 4.300 0.000 0.388 0.388 RIsi4 0.090 0.021 4.283 0.000 0.208 0.208 RIsi5 0.245 0.029 8.518 0.000 0.624 0.624 RIsi6 0.196 0.027 7.353 0.000 0.515 0.515 RIadhd7 ~~
RIadhd8 0.183 0.019 9.563 0.000 0.422 0.422 RIadhd9 0.292 0.021 13.870 0.000 0.639 0.639 RIadhd10 0.257 0.020 12.931 0.000 0.564 0.564 RIadhd11 0.185 0.019 9.629 0.000 0.420 0.420 RIadhd12 0.037 0.019 1.901 0.057 0.087 0.087 RIadhd13 0.173 0.020 8.703 0.000 0.394 0.394 RIadhd14 0.252 0.021 12.122 0.000 0.527 0.527 RIadhd15 0.280 0.021 13.245 0.000 0.572 0.572 RIadhd16 0.328 0.022 15.077 0.000 0.688 0.688 RIadhd17 0.163 0.019 8.512 0.000 0.328 0.328 RIadhd18 0.057 0.019 3.039 0.002 0.111 0.111 RIsi1 0.164 0.026 6.381 0.000 0.493 0.493 RIsi2 0.253 0.027 9.318 0.000 0.694 0.694 RIsi3 0.156 0.026 5.919 0.000 0.500 0.500 RIsi4 0.146 0.022 6.808 0.000 0.321 0.321 RIsi5 0.277 0.030 9.314 0.000 0.668 0.668 RIsi6 0.295 0.027 11.078 0.000 0.735 0.735 RIadhd8 ~~
RIadhd9 0.327 0.020 16.118 0.000 0.721 0.721 RIadhd10 0.239 0.019 12.408 0.000 0.529 0.529 RIadhd11 0.209 0.019 11.120 0.000 0.476 0.476 RIadhd12 0.108 0.019 5.681 0.000 0.255 0.255 RIadhd13 0.151 0.019 7.811 0.000 0.345 0.345 RIadhd14 0.184 0.020 9.093 0.000 0.387 0.387 RIadhd15 0.171 0.021 8.207 0.000 0.351 0.351 RIadhd16 0.168 0.022 7.761 0.000 0.354 0.354 RIadhd17 0.120 0.018 6.488 0.000 0.243 0.243 RIadhd18 0.130 0.017 7.536 0.000 0.256 0.256 RIsi1 0.129 0.026 4.987 0.000 0.390 0.390 RIsi2 0.208 0.026 7.969 0.000 0.574 0.574 RIsi3 0.119 0.026 4.595 0.000 0.383 0.383 RIsi4 0.074 0.021 3.533 0.000 0.162 0.162 RIsi5 0.221 0.028 7.860 0.000 0.537 0.537 RIsi6 0.122 0.026 4.614 0.000 0.305 0.305 RIadhd9 ~~
RIadhd10 0.297 0.021 14.318 0.000 0.627 0.627 RIadhd11 0.207 0.021 9.887 0.000 0.449 0.449 RIadhd12 0.112 0.021 5.309 0.000 0.253 0.253 RIadhd13 0.176 0.022 8.033 0.000 0.383 0.383 RIadhd14 0.271 0.023 11.999 0.000 0.542 0.542 RIadhd15 0.270 0.023 11.546 0.000 0.528 0.528 RIadhd16 0.316 0.024 13.268 0.000 0.634 0.634 RIadhd17 0.204 0.021 9.719 0.000 0.394 0.394 RIadhd18 0.138 0.020 7.043 0.000 0.260 0.260 RIsi1 0.178 0.030 6.005 0.000 0.513 0.513 RIsi2 0.238 0.030 7.992 0.000 0.625 0.625 RIsi3 0.143 0.029 4.872 0.000 0.439 0.439 RIsi4 0.154 0.023 6.619 0.000 0.325 0.325 RIsi5 0.263 0.033 8.065 0.000 0.607 0.607 RIsi6 0.272 0.029 9.283 0.000 0.649 0.649 RIadhd10 ~~
RIadhd11 0.335 0.020 17.024 0.000 0.730 0.730 RIadhd12 0.222 0.020 10.967 0.000 0.501 0.501 RIadhd13 0.295 0.020 14.686 0.000 0.646 0.646 RIadhd14 0.331 0.021 15.885 0.000 0.664 0.664 RIadhd15 0.357 0.021 16.859 0.000 0.702 0.702 RIadhd16 0.337 0.022 15.264 0.000 0.680 0.680 RIadhd17 0.348 0.018 18.949 0.000 0.673 0.673 RIadhd18 0.251 0.018 14.060 0.000 0.472 0.472 RIsi1 0.152 0.028 5.448 0.000 0.440 0.440 RIsi2 0.289 0.028 10.215 0.000 0.761 0.761 RIsi3 0.218 0.028 7.691 0.000 0.674 0.674 RIsi4 0.036 0.023 1.574 0.115 0.076 0.076 RIsi5 0.338 0.030 11.181 0.000 0.784 0.784 RIsi6 0.193 0.029 6.681 0.000 0.463 0.463 RIadhd11 ~~
RIadhd12 0.286 0.019 14.708 0.000 0.667 0.667 RIadhd13 0.358 0.019 18.793 0.000 0.808 0.808 RIadhd14 0.263 0.021 12.764 0.000 0.544 0.544 RIadhd15 0.300 0.021 14.387 0.000 0.608 0.608 RIadhd16 0.271 0.022 12.313 0.000 0.564 0.564 RIadhd17 0.310 0.018 17.112 0.000 0.619 0.619 RIadhd18 0.338 0.017 20.355 0.000 0.656 0.656 RIsi1 0.152 0.027 5.585 0.000 0.451 0.451 RIsi2 0.232 0.028 8.323 0.000 0.632 0.632 RIsi3 0.206 0.028 7.425 0.000 0.656 0.656 RIsi4 0.036 0.022 1.673 0.094 0.079 0.079 RIsi5 0.302 0.029 10.286 0.000 0.721 0.721 RIsi6 0.147 0.028 5.261 0.000 0.364 0.364 RIadhd12 ~~
RIadhd13 0.366 0.019 18.942 0.000 0.856 0.856 RIadhd14 0.170 0.021 8.083 0.000 0.366 0.366 RIadhd15 0.209 0.021 9.782 0.000 0.438 0.438 RIadhd16 0.172 0.022 7.731 0.000 0.371 0.371 RIadhd17 0.314 0.018 17.388 0.000 0.651 0.651 RIadhd18 0.336 0.016 20.655 0.000 0.676 0.676 RIsi1 0.115 0.027 4.269 0.000 0.355 0.355 RIsi2 0.156 0.029 5.430 0.000 0.439 0.439 RIsi3 0.154 0.028 5.510 0.000 0.508 0.508 RIsi4 0.015 0.022 0.700 0.484 0.035 0.035 RIsi5 0.199 0.030 6.644 0.000 0.494 0.494 RIsi6 0.043 0.028 1.526 0.127 0.111 0.111 RIadhd13 ~~
RIadhd14 0.254 0.021 12.092 0.000 0.527 0.527 RIadhd15 0.317 0.021 14.889 0.000 0.644 0.644 RIadhd16 0.313 0.023 13.784 0.000 0.652 0.652 RIadhd17 0.347 0.018 19.203 0.000 0.695 0.695 RIadhd18 0.293 0.017 17.556 0.000 0.569 0.569 RIsi1 0.155 0.027 5.720 0.000 0.461 0.461 RIsi2 0.276 0.028 9.836 0.000 0.754 0.754 RIsi3 0.219 0.028 7.916 0.000 0.700 0.700 RIsi4 0.040 0.022 1.807 0.071 0.087 0.087 RIsi5 0.305 0.030 10.221 0.000 0.731 0.731 RIsi6 0.152 0.028 5.391 0.000 0.377 0.377 RIadhd14 ~~
RIadhd15 0.535 0.021 25.223 0.000 0.997 0.997 RIadhd16 0.417 0.023 18.255 0.000 0.798 0.798 RIadhd17 0.394 0.019 21.084 0.000 0.724 0.724 RIadhd18 0.228 0.019 12.278 0.000 0.407 0.407 RIsi1 0.147 0.029 5.078 0.000 0.403 0.403 RIsi2 0.242 0.030 8.067 0.000 0.605 0.605 RIsi3 0.153 0.029 5.239 0.000 0.449 0.449 RIsi4 0.075 0.023 3.213 0.001 0.151 0.151 RIsi5 0.265 0.031 8.490 0.000 0.582 0.582 RIsi6 0.201 0.029 6.826 0.000 0.457 0.457 RIadhd15 ~~
RIadhd16 0.526 0.023 22.814 0.000 0.986 0.986 RIadhd17 0.487 0.018 26.458 0.000 0.876 0.876 RIadhd18 0.265 0.019 13.886 0.000 0.463 0.463 RIsi1 0.146 0.030 4.906 0.000 0.390 0.390 RIsi2 0.276 0.031 8.987 0.000 0.676 0.676 RIsi3 0.159 0.030 5.265 0.000 0.455 0.455 RIsi4 0.087 0.024 3.635 0.000 0.170 0.170 RIsi5 0.297 0.032 9.197 0.000 0.640 0.640 RIsi6 0.219 0.030 7.244 0.000 0.487 0.487 RIadhd16 ~~
RIadhd17 0.450 0.020 22.495 0.000 0.830 0.830 RIadhd18 0.226 0.020 11.120 0.000 0.406 0.406 RIsi1 0.175 0.031 5.696 0.000 0.481 0.481 RIsi2 0.257 0.032 8.128 0.000 0.645 0.645 RIsi3 0.174 0.031 5.624 0.000 0.511 0.511 RIsi4 0.090 0.025 3.610 0.000 0.181 0.181 RIsi5 0.283 0.033 8.508 0.000 0.625 0.625 RIsi6 0.228 0.031 7.322 0.000 0.520 0.520 RIadhd17 ~~
RIadhd18 0.334 0.017 19.881 0.000 0.575 0.575 RIsi1 0.102 0.025 4.122 0.000 0.269 0.269 RIsi2 0.204 0.025 8.176 0.000 0.492 0.492 RIsi3 0.157 0.024 6.557 0.000 0.444 0.444 RIsi4 0.028 0.022 1.308 0.191 0.055 0.055 RIsi5 0.230 0.028 8.306 0.000 0.489 0.489 RIsi6 0.116 0.027 4.329 0.000 0.254 0.254 RIadhd18 ~~
RIsi1 0.120 0.023 5.303 0.000 0.307 0.307 RIsi2 0.121 0.023 5.304 0.000 0.284 0.284 RIsi3 0.155 0.021 7.414 0.000 0.426 0.426 RIsi4 -0.023 0.020 -1.179 0.238 -0.044 -0.044 RIsi5 0.167 0.026 6.418 0.000 0.345 0.345 RIsi6 -0.025 0.025 -1.026 0.305 -0.054 -0.054 RIsi1 ~~
RIsi2 0.095 0.066 1.426 0.154 0.340 0.340 RIsi3 0.111 0.066 1.685 0.092 0.466 0.466 RIsi4 0.093 0.046 2.017 0.044 0.267 0.267 RIsi5 0.107 0.070 1.529 0.126 0.337 0.337 RIsi6 0.133 0.062 2.141 0.032 0.434 0.434 RIsi2 ~~
RIsi3 0.011 0.069 0.156 0.876 0.041 0.041 RIsi4 0.180 0.048 3.725 0.000 0.474 0.474 RIsi5 0.343 0.075 4.548 0.000 0.990 0.990 RIsi6 0.214 0.067 3.190 0.001 0.638 0.638 RIsi3 ~~
RIsi4 -0.056 0.046 -1.210 0.226 -0.172 -0.172 RIsi5 0.020 0.073 0.272 0.785 0.067 0.067 RIsi6 -0.007 0.063 -0.116 0.908 -0.026 -0.026 RIsi4 ~~
RIsi5 0.150 0.052 2.883 0.004 0.348 0.348 RIsi6 0.346 0.047 7.373 0.000 0.828 0.828 RIsi5 ~~
RIsi6 0.210 0.071 2.961 0.003 0.550 0.550

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 (y) -0.005 0.010 -0.495 0.620 -0.005 -0.005 .pe81m7 (y) -0.005 0.010 -0.495 0.620 -0.005 -0.005 .pe81m10 (y) -0.005 0.010 -0.495 0.620 -0.005 -0.005 .pe81m12 (y) -0.005 0.010 -0.495 0.620 -0.005 -0.005 .pe82m5 (z) -0.001 0.010 -0.095 0.924 -0.001 -0.001 .pe82m7 (z) -0.001 0.010 -0.095 0.924 -0.001 -0.001 .pe82m10 (z) -0.001 0.010 -0.095 0.924 -0.001 -0.001 .pe82m12 (z) -0.001 0.010 -0.095 0.924 -0.001 -0.001 .pe83m5 (aa) -0.004 0.009 -0.488 0.625 -0.004 -0.004 .pe83m7 (aa) -0.004 0.009 -0.488 0.625 -0.004 -0.004 .pe83m10 (aa) -0.004 0.009 -0.488 0.625 -0.004 -0.004 .pe83m12 (aa) -0.004 0.009 -0.488 0.625 -0.004 -0.004 .pe86m5 (ab) -0.002 0.009 -0.243 0.808 -0.002 -0.002 .pe86m7 (ab) -0.002 0.009 -0.243 0.808 -0.002 -0.002 .pe86m10 (ab) -0.002 0.009 -0.243 0.808 -0.002 -0.002 .pe86m12 (ab) -0.002 0.009 -0.243 0.808 -0.002 -0.002 .pe87m5 (ac) -0.007 0.012 -0.580 0.562 -0.007 -0.007 .pe87m7 (ac) -0.007 0.012 -0.580 0.562 -0.007 -0.007 .pe87m10 (ac) -0.007 0.012 -0.580 0.562 -0.007 -0.007 .pe87m12 (ac) -0.007 0.012 -0.580 0.562 -0.007 -0.007 .pe88m5 (ad) 0.008 0.010 0.809 0.418 0.008 0.008 .pe88m7 (ad) 0.008 0.010 0.809 0.418 0.008 0.008 .pe88m10 (ad) 0.008 0.010 0.809 0.418 0.008 0.008 .pe88m12 (ad) 0.008 0.010 0.809 0.418 0.008 0.008 .pe89m5 (ae) -0.073 0.011 -6.638 0.000 -0.073 -0.073 .pe89m7 (ae) -0.073 0.011 -6.638 0.000 -0.073 -0.073 .pe89m10 (ae) -0.073 0.011 -6.638 0.000 -0.073 -0.073 .pe89m12 (ae) -0.073 0.011 -6.638 0.000 -0.073 -0.073 .pe90m5 (af) -0.005 0.011 -0.465 0.642 -0.005 -0.005 .pe90m7 (af) -0.005 0.011 -0.465 0.642 -0.005 -0.005 .pe90m10 (af) -0.005 0.011 -0.465 0.642 -0.005 -0.005 .pe90m12 (af) -0.005 0.011 -0.465 0.642 -0.005 -0.005 .pe91m5 (ag) 0.012 0.012 0.991 0.322 0.012 0.012 .pe91m7 (ag) 0.012 0.012 0.991 0.322 0.012 0.012 .pe91m10 (ag) 0.012 0.012 0.991 0.322 0.012 0.012 .pe91m12 (ag) 0.012 0.012 0.991 0.322 0.012 0.012 .pe84m5 (ah) 0.012 0.010 1.269 0.204 0.012 0.012 .pe84m7 (ah) 0.012 0.010 1.269 0.204 0.012 0.012 .pe84m10 (ah) 0.012 0.010 1.269 0.204 0.012 0.012 .pe84m12 (ah) 0.012 0.010 1.269 0.204 0.012 0.012 .pe85m5 (ai) -0.007 0.010 -0.737 0.461 -0.007 -0.007 .pe85m7 (ai) -0.007 0.010 -0.737 0.461 -0.007 -0.007 .pe85m10 (ai) -0.007 0.010 -0.737 0.461 -0.007 -0.007 .pe85m12 (ai) -0.007 0.010 -0.737 0.461 -0.007 -0.007 .pe96m5 (aj) -0.002 0.011 -0.217 0.828 -0.002 -0.002 .pe96m7 (aj) -0.002 0.011 -0.217 0.828 -0.002 -0.002 .pe96m10 (aj) -0.002 0.011 -0.217 0.828 -0.002 -0.002 .pe96m12 (aj) -0.002 0.011 -0.217 0.828 -0.002 -0.002 .pe97m5 (ak) -0.007 0.010 -0.746 0.456 -0.007 -0.007 .pe97m7 (ak) -0.007 0.010 -0.746 0.456 -0.007 -0.007 .pe97m10 (ak) -0.007 0.010 -0.746 0.456 -0.007 -0.007 .pe97m12 (ak) -0.007 0.010 -0.746 0.456 -0.007 -0.007 .pe92m5 (al) -0.002 0.010 -0.155 0.877 -0.002 -0.002 .pe92m7 (al) -0.002 0.010 -0.155 0.877 -0.002 -0.002 .pe92m10 (al) -0.002 0.010 -0.155 0.877 -0.002 -0.002 .pe92m12 (al) -0.002 0.010 -0.155 0.877 -0.002 -0.002 .pe93m5 (am) -0.004 0.010 -0.428 0.668 -0.004 -0.004 .pe93m7 (am) -0.004 0.010 -0.428 0.668 -0.004 -0.004 .pe93m10 (am) -0.004 0.010 -0.428 0.668 -0.004 -0.004 .pe93m12 (am) -0.004 0.010 -0.428 0.668 -0.004 -0.004 .pe94m5 (an) -0.003 0.012 -0.238 0.812 -0.003 -0.003 .pe94m7 (an) -0.003 0.012 -0.238 0.812 -0.003 -0.003 .pe94m10 (an) -0.003 0.012 -0.238 0.812 -0.003 -0.003 .pe94m12 (an) -0.003 0.012 -0.238 0.812 -0.003 -0.003 .pe95m5 (ao) 0.003 0.011 0.232 0.816 0.003 0.003 .pe95m7 (ao) 0.003 0.011 0.232 0.816 0.003 0.003 .pe95m10 (ao) 0.003 0.011 0.232 0.816 0.003 0.003 .pe95m12 (ao) 0.003 0.011 0.232 0.816 0.003 0.003 .pe64m5 (ap) 0.001 0.012 0.050 0.960 0.001 0.001 .pe64m7 (ap) 0.001 0.012 0.050 0.960 0.001 0.001 .pe64m10 (ap) 0.001 0.012 0.050 0.960 0.001 0.001 .pe64m12 (ap) 0.001 0.012 0.050 0.960 0.001 0.001 .pe2m5 (aq) -0.009 0.015 -0.600 0.549 -0.009 -0.009 .pe2m7 (aq) -0.009 0.015 -0.600 0.549 -0.009 -0.009 .pe2m10 (aq) -0.009 0.015 -0.600 0.549 -0.009 -0.009 .pe2m12 (aq) -0.009 0.015 -0.600 0.549 -0.009 -0.009 .pe4m5 (ar) -0.031 0.014 -2.194 0.028 -0.031 -0.031 .pe4m7 (ar) -0.031 0.014 -2.194 0.028 -0.031 -0.031 .pe4m10 (ar) -0.031 0.014 -2.194 0.028 -0.031 -0.031 .pe4m12 (ar) -0.031 0.014 -2.194 0.028 -0.031 -0.031 .pe7m5 (as) -0.006 0.015 -0.386 0.700 -0.006 -0.006 .pe7m7 (as) -0.006 0.015 -0.386 0.700 -0.006 -0.006 .pe7m10 (as) -0.006 0.015 -0.386 0.700 -0.006 -0.006 .pe7m12 (as) -0.006 0.015 -0.386 0.700 -0.006 -0.006 .pe11m5 (at) -0.015 0.015 -0.991 0.322 -0.015 -0.015 .pe11m7 (at) -0.015 0.015 -0.991 0.322 -0.015 -0.015 .pe11m10 (at) -0.015 0.015 -0.991 0.322 -0.015 -0.015 .pe11m12 (at) -0.015 0.015 -0.991 0.322 -0.015 -0.015 .pe13m5 (au) -0.004 0.021 -0.189 0.850 -0.004 -0.004 .pe13m7 (au) -0.004 0.021 -0.189 0.850 -0.004 -0.004 .pe13m10 (au) -0.004 0.021 -0.189 0.850 -0.004 -0.004 .pe13m12 (au) -0.004 0.021 -0.189 0.850 -0.004 -0.004 .pe25m5 (av) -0.004 0.018 -0.209 0.834 -0.004 -0.004 .pe25m7 (av) -0.004 0.018 -0.209 0.834 -0.004 -0.004 .pe25m10 (av) -0.004 0.018 -0.209 0.834 -0.004 -0.004 .pe25m12 (av) -0.004 0.018 -0.209 0.834 -0.004 -0.004 .WFadhd7 0.005 0.015 0.318 0.750 0.009 0.009 .WFadhd10 0.004 0.015 0.258 0.796 0.007 0.007 .WFadhd12 0.003 0.013 0.215 0.830 0.005 0.005 .WFsi7 0.012 0.024 0.490 0.624 0.019 0.019 .WFsi10 0.005 0.021 0.238 0.812 0.008 0.008 .WFsi12 0.002 0.022 0.102 0.918 0.003 0.003 RIadhd1 0.000 0.000 0.000 RIadhd2 0.000 0.000 0.000 RIadhd3 0.000 0.000 0.000 RIadhd4 0.000 0.000 0.000 RIadhd5 0.000 0.000 0.000 RIadhd6 0.000 0.000 0.000 RIadhd7 0.000 0.000 0.000 RIadhd8 0.000 0.000 0.000 RIadhd9 0.000 0.000 0.000 RIadhd10 0.000 0.000 0.000 RIadhd11 0.000 0.000 0.000 RIadhd12 0.000 0.000 0.000 RIadhd13 0.000 0.000 0.000 RIadhd14 0.000 0.000 0.000 RIadhd15 0.000 0.000 0.000 RIadhd16 0.000 0.000 0.000 RIadhd17 0.000 0.000 0.000 RIadhd18 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFadhd5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5|t1 0.254 0.022 11.675 0.000 0.254 0.254 pe81m5|t2 1.092 0.027 39.856 0.000 1.092 1.092 pe81m7|t1 0.306 0.019 15.713 0.000 0.306 0.306 pe81m7|t2 1.182 0.024 49.858 0.000 1.182 1.182 pe81m10|t1 0.392 0.019 20.477 0.000 0.392 0.392 pe81m10|t2 1.215 0.024 50.811 0.000 1.215 1.215 pe81m12|t1 0.428 0.019 22.399 0.000 0.428 0.428 pe81m12|t2 1.252 0.024 52.402 0.000 1.252 1.252 pe82m5|t1 0.097 0.022 4.394 0.000 0.097 0.097 pe82m5|t2 1.070 0.027 39.002 0.000 1.070 1.070 pe82m7|t1 0.198 0.021 9.552 0.000 0.198 0.198 pe82m7|t2 1.262 0.026 49.011 0.000 1.262 1.262 pe82m10|t1 0.231 0.021 10.853 0.000 0.231 0.231 pe82m10|t2 1.333 0.026 50.513 0.000 1.333 1.333 pe82m12|t1 0.313 0.021 15.178 0.000 0.313 0.313 pe82m12|t2 1.375 0.027 51.321 0.000 1.375 1.375 pe83m5|t1 -0.115 0.022 -5.280 0.000 -0.115 -0.115 pe83m5|t2 0.758 0.024 31.223 0.000 0.758 0.758 pe83m7|t1 -0.024 0.020 -1.219 0.223 -0.024 -0.024 pe83m7|t2 0.964 0.021 45.121 0.000 0.964 0.964 pe83m10|t1 -0.024 0.020 -1.177 0.239 -0.024 -0.024 pe83m10|t2 1.013 0.022 46.795 0.000 1.013 1.013 pe83m12|t1 0.053 0.020 2.628 0.009 0.053 0.053 pe83m12|t2 1.099 0.022 49.537 0.000 1.099 1.099 pe86m5|t1 0.060 0.022 2.684 0.007 0.060 0.060 pe86m5|t2 1.137 0.029 39.761 0.000 1.137 1.137 pe86m7|t1 0.149 0.021 7.238 0.000 0.149 0.149 pe86m7|t2 1.309 0.027 48.013 0.000 1.309 1.309 pe86m10|t1 0.157 0.021 7.553 0.000 0.157 0.157 pe86m10|t2 1.289 0.027 47.285 0.000 1.289 1.289 pe86m12|t1 0.083 0.021 3.908 0.000 0.083 0.083 pe86m12|t2 1.344 0.028 48.567 0.000 1.344 1.344 pe87m5|t1 0.309 0.023 13.347 0.000 0.309 0.309 pe87m5|t2 1.489 0.035 42.044 0.000 1.489 1.489 pe87m7|t1 0.263 0.022 11.800 0.000 0.263 0.263 pe87m7|t2 1.636 0.037 44.158 0.000 1.636 1.636 pe87m10|t1 0.403 0.023 17.691 0.000 0.403 0.403 pe87m10|t2 1.762 0.040 43.898 0.000 1.762 1.762 pe87m12|t1 0.364 0.022 16.463 0.000 0.364 0.364 pe87m12|t2 1.695 0.038 44.492 0.000 1.695 1.695 pe88m5|t1 0.411 0.023 17.995 0.000 0.411 0.411 pe88m5|t2 1.146 0.029 39.490 0.000 1.146 1.146 pe88m7|t1 0.339 0.021 15.996 0.000 0.339 0.339 pe88m7|t2 1.295 0.028 45.793 0.000 1.295 1.295 pe88m10|t1 0.068 0.021 3.229 0.001 0.068 0.068 pe88m10|t2 1.026 0.024 42.562 0.000 1.026 1.026 pe88m12|t1 0.023 0.022 1.074 0.283 0.023 0.023 pe88m12|t2 0.956 0.023 41.010 0.000 0.956 0.956 pe89m5|t1 0.405 0.023 17.539 0.000 0.405 0.405 pe89m5|t2 1.217 0.031 39.477 0.000 1.217 1.217 pe89m7|t1 0.429 0.021 20.284 0.000 0.429 0.429 pe89m7|t2 1.338 0.031 43.674 0.000 1.338 1.338 pe89m10|t1 0.395 0.022 18.320 0.000 0.395 0.395 pe89m10|t2 1.330 0.030 43.755 0.000 1.330 1.330 pe89m12|t1 0.393 0.022 18.058 0.000 0.393 0.393 pe89m12|t2 1.315 0.030 44.385 0.000 1.315 1.315 pe90m5|t1 0.050 0.022 2.281 0.023 0.050 0.050 pe90m5|t2 0.802 0.025 32.310 0.000 0.802 0.802 pe90m7|t1 0.044 0.021 2.050 0.040 0.044 0.044 pe90m7|t2 0.986 0.024 40.946 0.000 0.986 0.986 pe90m10|t1 0.040 0.021 1.926 0.054 0.040 0.040 pe90m10|t2 0.938 0.024 39.331 0.000 0.938 0.938 pe90m12|t1 0.078 0.021 3.692 0.000 0.078 0.078 pe90m12|t2 0.969 0.024 40.388 0.000 0.969 0.969 pe91m5|t1 0.591 0.023 25.321 0.000 0.591 0.591 pe91m5|t2 1.455 0.033 43.656 0.000 1.455 1.455 pe91m7|t1 0.586 0.021 27.337 0.000 0.586 0.586 pe91m7|t2 1.550 0.031 49.271 0.000 1.550 1.550 pe91m10|t1 0.664 0.022 29.874 0.000 0.664 0.664 pe91m10|t2 1.621 0.034 47.296 0.000 1.621 1.621 pe91m12|t1 0.589 0.022 27.379 0.000 0.589 0.589 pe91m12|t2 1.640 0.035 47.070 0.000 1.640 1.640 pe84m5|t1 0.011 0.022 0.493 0.622 0.011 0.011 pe84m5|t2 0.985 0.026 37.615 0.000 0.985 0.985 pe84m7|t1 0.094 0.021 4.551 0.000 0.094 0.094 pe84m7|t2 1.248 0.026 48.056 0.000 1.248 1.248 pe84m10|t1 0.130 0.021 6.291 0.000 0.130 0.130 pe84m10|t2 1.275 0.026 48.627 0.000 1.275 1.275 pe84m12|t1 0.110 0.021 5.260 0.000 0.110 0.110 pe84m12|t2 1.314 0.026 49.813 0.000 1.314 1.314 pe85m5|t1 -0.939 0.026 -35.421 0.000 -0.939 -0.939 pe85m5|t2 0.259 0.023 11.510 0.000 0.259 0.259 pe85m7|t1 -0.868 0.026 -33.396 0.000 -0.868 -0.868 pe85m7|t2 0.583 0.021 28.144 0.000 0.583 0.583 pe85m10|t1 -0.482 0.022 -21.761 0.000 -0.482 -0.482 pe85m10|t2 0.862 0.023 37.939 0.000 0.862 0.862 pe85m12|t1 -0.291 0.022 -13.405 0.000 -0.291 -0.291 pe85m12|t2 0.949 0.023 40.960 0.000 0.949 0.949 pe96m5|t1 -0.190 0.022 -8.562 0.000 -0.190 -0.190 pe96m5|t2 0.847 0.026 33.145 0.000 0.847 0.847 pe96m7|t1 -0.167 0.022 -7.513 0.000 -0.167 -0.167 pe96m7|t2 0.925 0.024 38.338 0.000 0.925 0.925 pe96m10|t1 -0.034 0.021 -1.599 0.110 -0.034 -0.034 pe96m10|t2 1.075 0.025 42.476 0.000 1.075 1.075 pe96m12|t1 0.060 0.021 2.823 0.005 0.060 0.060 pe96m12|t2 1.121 0.024 45.830 0.000 1.121 1.121 pe97m5|t1 -0.365 0.023 -16.092 0.000 -0.365 -0.365 pe97m5|t2 0.598 0.024 25.322 0.000 0.598 0.598 pe97m7|t1 -0.271 0.022 -12.364 0.000 -0.271 -0.271 pe97m7|t2 0.789 0.022 36.155 0.000 0.789 0.789 pe97m10|t1 0.137 0.021 6.638 0.000 0.137 0.137 pe97m10|t2 1.108 0.025 44.787 0.000 1.108 1.108 pe97m12|t1 0.254 0.020 12.511 0.000 0.254 0.254 pe97m12|t2 1.168 0.025 47.116 0.000 1.168 1.168 pe92m5|t1 0.003 0.021 0.138 0.890 0.003 0.003 pe92m5|t2 0.808 0.024 33.192 0.000 0.808 0.808 pe92m7|t1 0.062 0.020 3.126 0.002 0.062 0.062 pe92m7|t2 0.962 0.022 44.000 0.000 0.962 0.962 pe92m10|t1 0.370 0.019 19.415 0.000 0.370 0.370 pe92m10|t2 1.157 0.023 49.897 0.000 1.157 1.157 pe92m12|t1 0.409 0.020 20.968 0.000 0.409 0.409 pe92m12|t2 1.252 0.025 49.999 0.000 1.252 1.252 pe93m5|t1 0.194 0.021 9.063 0.000 0.194 0.194 pe93m5|t2 0.870 0.024 35.910 0.000 0.870 0.870 pe93m7|t1 0.356 0.019 18.887 0.000 0.356 0.356 pe93m7|t2 1.003 0.021 47.738 0.000 1.003 1.003 pe93m10|t1 0.518 0.018 28.068 0.000 0.518 0.518 pe93m10|t2 1.240 0.024 51.335 0.000 1.240 1.240 pe93m12|t1 0.599 0.019 31.141 0.000 0.599 0.599 pe93m12|t2 1.300 0.025 51.823 0.000 1.300 1.300 pe94m5|t1 0.581 0.023 25.626 0.000 0.581 0.581 pe94m5|t2 1.227 0.028 43.175 0.000 1.227 1.227 pe94m7|t1 0.720 0.020 36.557 0.000 0.720 0.720 pe94m7|t2 1.377 0.027 51.733 0.000 1.377 1.377 pe94m10|t1 0.817 0.021 39.650 0.000 0.817 0.817 pe94m10|t2 1.559 0.032 49.370 0.000 1.559 1.559 pe94m12|t1 0.893 0.021 43.497 0.000 0.893 0.893 pe94m12|t2 1.592 0.032 50.351 0.000 1.592 1.592 pe95m5|t1 -0.003 0.021 -0.142 0.887 -0.003 -0.003 pe95m5|t2 0.512 0.022 23.347 0.000 0.512 0.512 pe95m7|t1 0.155 0.020 7.935 0.000 0.155 0.155 pe95m7|t2 0.773 0.021 37.198 0.000 0.773 0.773 pe95m10|t1 0.294 0.019 15.118 0.000 0.294 0.294 pe95m10|t2 0.947 0.023 41.546 0.000 0.947 0.947 pe95m12|t1 0.472 0.019 24.522 0.000 0.472 0.472 pe95m12|t2 1.086 0.025 44.041 0.000 1.086 1.086 pe64m5|t1 -0.245 0.021 -11.648 0.000 -0.245 -0.245 pe64m5|t2 0.491 0.022 22.579 0.000 0.491 0.491 pe64m7|t1 -0.280 0.021 -13.224 0.000 -0.280 -0.280 pe64m7|t2 0.638 0.022 29.110 0.000 0.638 0.638 pe64m10|t1 -0.071 0.020 -3.503 0.000 -0.071 -0.071 pe64m10|t2 0.873 0.023 37.198 0.000 0.873 0.873 pe64m12|t1 -0.042 0.020 -2.124 0.034 -0.042 -0.042 pe64m12|t2 0.876 0.023 37.536 0.000 0.876 0.876 pe2m5|t1 1.356 0.033 41.186 0.000 1.356 1.356 pe2m5|t2 2.358 0.074 31.990 0.000 2.358 2.358 pe2m7|t1 1.178 0.032 36.484 0.000 1.178 1.178 pe2m7|t2 2.262 0.058 38.972 0.000 2.262 2.262 pe2m10|t1 1.009 0.030 33.133 0.000 1.009 1.009 pe2m10|t2 2.138 0.053 40.682 0.000 2.138 2.138 pe2m12|t1 1.132 0.031 36.632 0.000 1.132 1.132 pe2m12|t2 2.241 0.057 39.350 0.000 2.241 2.241 pe4m5|t1 0.976 0.027 36.114 0.000 0.976 0.976 pe4m5|t2 2.098 0.057 36.925 0.000 2.098 2.098 pe4m7|t1 0.994 0.031 31.912 0.000 0.994 0.994 pe4m7|t2 2.241 0.055 40.925 0.000 2.241 2.241 pe4m10|t1 0.899 0.030 29.819 0.000 0.899 0.899 pe4m10|t2 2.129 0.047 45.179 0.000 2.129 2.129 pe4m12|t1 0.884 0.032 27.495 0.000 0.884 0.884 pe4m12|t2 2.206 0.052 42.703 0.000 2.206 2.206 pe7m5|t1 0.828 0.026 31.440 0.000 0.828 0.828 pe7m5|t2 1.952 0.049 39.473 0.000 1.952 1.952 pe7m7|t1 0.669 0.029 23.442 0.000 0.669 0.669 pe7m7|t2 1.839 0.041 44.743 0.000 1.839 1.839 pe7m10|t1 0.658 0.028 23.469 0.000 0.658 0.658 pe7m10|t2 1.948 0.044 44.210 0.000 1.948 1.948 pe7m12|t1 0.725 0.029 25.405 0.000 0.725 0.725 pe7m12|t2 2.014 0.047 42.796 0.000 2.014 2.014 pe11m5|t1 0.674 0.025 27.204 0.000 0.674 0.674 pe11m5|t2 1.769 0.042 42.410 0.000 1.769 1.769 pe11m7|t1 0.852 0.027 32.056 0.000 0.852 0.852 pe11m7|t2 1.883 0.042 44.984 0.000 1.883 1.883 pe11m10|t1 0.788 0.026 30.094 0.000 0.788 0.788 pe11m10|t2 1.973 0.046 42.579 0.000 1.973 1.973 pe11m12|t1 0.857 0.027 31.699 0.000 0.857 0.857 pe11m12|t2 1.991 0.047 42.292 0.000 1.991 1.991 pe13m5|t1 1.572 0.037 43.036 0.000 1.572 1.572 pe13m5|t2 2.645 0.099 26.747 0.000 2.645 2.645 pe13m7|t1 1.466 0.037 39.923 0.000 1.466 1.466 pe13m7|t2 2.614 0.083 31.623 0.000 2.614 2.614 pe13m10|t1 1.262 0.035 36.326 0.000 1.262 1.262 pe13m10|t2 2.400 0.062 38.407 0.000 2.400 2.400 pe13m12|t1 1.207 0.035 34.593 0.000 1.207 1.207 pe13m12|t2 2.292 0.056 40.666 0.000 2.292 2.292 pe25m5|t1 1.168 0.030 38.918 0.000 1.168 1.168 pe25m5|t2 2.279 0.066 34.757 0.000 2.279 2.279 pe25m7|t1 1.392 0.034 40.652 0.000 1.392 1.392 pe25m7|t2 2.331 0.061 38.248 0.000 2.331 2.331 pe25m10|t1 1.377 0.031 44.046 0.000 1.377 1.377 pe25m10|t2 2.341 0.063 36.982 0.000 2.341 2.341 pe25m12|t1 1.264 0.032 38.972 0.000 1.264 1.264 pe25m12|t2 2.466 0.074 33.477 0.000 2.466 2.466

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 0.244 0.244 0.244 .pe81m7 0.202 0.202 0.202 .pe81m10 0.181 0.181 0.181 .pe81m12 0.120 0.120 0.120 .pe82m5 0.329 0.329 0.329 .pe82m7 0.285 0.285 0.285 .pe82m10 0.264 0.264 0.264 .pe82m12 0.200 0.200 0.200 .pe83m5 0.290 0.290 0.290 .pe83m7 0.245 0.245 0.245 .pe83m10 0.224 0.224 0.224 .pe83m12 0.159 0.159 0.159 .pe86m5 0.361 0.361 0.361 .pe86m7 0.327 0.327 0.327 .pe86m10 0.311 0.311 0.311 .pe86m12 0.261 0.261 0.261 .pe87m5 0.408 0.408 0.408 .pe87m7 0.382 0.382 0.382 .pe87m10 0.370 0.370 0.370 .pe87m12 0.332 0.332 0.332 .pe88m5 0.449 0.449 0.449 .pe88m7 0.422 0.422 0.422 .pe88m10 0.409 0.409 0.409 .pe88m12 0.369 0.369 0.369 .pe89m5 0.418 0.418 0.418 .pe89m7 0.393 0.393 0.393 .pe89m10 0.381 0.381 0.381 .pe89m12 0.345 0.345 0.345 .pe90m5 0.438 0.438 0.438 .pe90m7 0.416 0.416 0.416 .pe90m10 0.405 0.405 0.405 .pe90m12 0.373 0.373 0.373 .pe91m5 0.349 0.349 0.349 .pe91m7 0.319 0.319 0.319 .pe91m10 0.304 0.304 0.304 .pe91m12 0.260 0.260 0.260 .pe84m5 0.336 0.336 0.336 .pe84m7 0.303 0.303 0.303 .pe84m10 0.287 0.287 0.287 .pe84m12 0.239 0.239 0.239 .pe85m5 0.371 0.371 0.371 .pe85m7 0.340 0.340 0.340 .pe85m10 0.325 0.325 0.325 .pe85m12 0.278 0.278 0.278 .pe96m5 0.394 0.394 0.394 .pe96m7 0.361 0.361 0.361 .pe96m10 0.345 0.345 0.345 .pe96m12 0.296 0.296 0.296 .pe97m5 0.369 0.369 0.369 .pe97m7 0.337 0.337 0.337 .pe97m10 0.321 0.321 0.321 .pe97m12 0.274 0.274 0.274 .pe92m5 0.271 0.271 0.271 .pe92m7 0.237 0.237 0.237 .pe92m10 0.220 0.220 0.220 .pe92m12 0.169 0.169 0.169 .pe93m5 0.243 0.243 0.243 .pe93m7 0.207 0.207 0.207 .pe93m10 0.190 0.190 0.190 .pe93m12 0.138 0.138 0.138 .pe94m5 0.259 0.259 0.259 .pe94m7 0.221 0.221 0.221 .pe94m10 0.203 0.203 0.203 .pe94m12 0.147 0.147 0.147 .pe95m5 0.336 0.336 0.336 .pe95m7 0.319 0.319 0.319 .pe95m10 0.311 0.311 0.311 .pe95m12 0.286 0.286 0.286 .pe64m5 0.342 0.342 0.342 .pe64m7 0.332 0.332 0.332 .pe64m10 0.327 0.327 0.327 .pe64m12 0.312 0.312 0.312 .pe2m5 0.435 0.435 0.435 .pe2m7 0.358 0.358 0.358 .pe2m10 0.365 0.365 0.365 .pe2m12 0.320 0.320 0.320 .pe4m5 0.321 0.321 0.321 .pe4m7 0.229 0.229 0.229 .pe4m10 0.237 0.237 0.237 .pe4m12 0.184 0.184 0.184 .pe7m5 0.399 0.399 0.399 .pe7m7 0.305 0.305 0.305 .pe7m10 0.313 0.313 0.313 .pe7m12 0.259 0.259 0.259 .pe11m5 0.380 0.380 0.380 .pe11m7 0.344 0.344 0.344 .pe11m10 0.347 0.347 0.347 .pe11m12 0.326 0.326 0.326 .pe13m5 0.237 0.237 0.237 .pe13m7 0.146 0.146 0.146 .pe13m10 0.154 0.154 0.154 .pe13m12 0.102 0.102 0.102 .pe25m5 0.319 0.319 0.319 .pe25m7 0.241 0.241 0.241 .pe25m10 0.249 0.249 0.249 .pe25m12 0.204 0.204 0.204 RIadhd1 0.511 0.024 21.641 0.000 1.000 1.000 RIadhd2 0.415 0.025 16.809 0.000 1.000 1.000 RIadhd3 0.452 0.024 18.607 0.000 1.000 1.000 RIadhd4 0.440 0.022 20.126 0.000 1.000 1.000 RIadhd5 0.443 0.020 21.983 0.000 1.000 1.000 RIadhd6 0.393 0.021 18.860 0.000 1.000 1.000 RIadhd7 0.438 0.021 20.953 0.000 1.000 1.000 RIadhd8 0.432 0.019 22.947 0.000 1.000 1.000 RIadhd9 0.476 0.023 20.438 0.000 1.000 1.000 RIadhd10 0.472 0.021 22.719 0.000 1.000 1.000 RIadhd11 0.445 0.020 22.134 0.000 1.000 1.000 RIadhd12 0.413 0.021 20.125 0.000 1.000 1.000 RIadhd13 0.442 0.021 21.220 0.000 1.000 1.000 RIadhd14 0.525 0.022 24.303 0.000 1.000 1.000 RIadhd15 0.548 0.023 24.044 0.000 1.000 1.000 RIadhd16 0.520 0.026 20.279 0.000 1.000 1.000 RIadhd17 0.564 0.017 33.343 0.000 1.000 1.000 RIadhd18 0.598 0.014 43.716 0.000 1.000 1.000 RIsi1 0.254 0.068 3.769 0.000 1.000 1.000 RIsi2 0.304 0.074 4.089 0.000 1.000 1.000 RIsi3 0.222 0.072 3.085 0.002 1.000 1.000 RIsi4 0.475 0.037 12.852 0.000 1.000 1.000 RIsi5 0.394 0.080 4.947 0.000 1.000 1.000 RIsi6 0.368 0.067 5.468 0.000 1.000 1.000 WFadhd5 0.245 0.023 10.773 0.000 1.000 1.000 .WFadhd7 0.245 0.015 15.882 0.000 0.851 0.851 .WFadhd10 0.251 0.016 16.049 0.000 0.814 0.814 .WFadhd12 0.190 0.013 14.774 0.000 0.515 0.515 WFsi5 0.311 0.071 4.354 0.000 1.000 1.000 .WFsi7 0.246 0.038 6.520 0.000 0.636 0.636 .WFsi10 0.240 0.036 6.608 0.000 0.631 0.631 .WFsi12 0.173 0.029 5.990 0.000 0.408 0.408

S3 Model fit: Comparative Fit Index (CFI) 0.984 (>0.95) Change in CFI: 0.00 (decrease) - same fit Tucker-Lewis Index (TLI) 0.983 (>0.95) Change in TLI: 0.00 (decrease) - same fit
RMSEA 0.017 (≤ 0.06) Change in RMSEA: 0.00 (increase) - same fit
90 Percent confidence interval - lower 0.016 90 Percent confidence interval - upper 0.018
SRMR 0.038 (≤ 0.08) Change in SRMR: 0.00 (increase) - same fit

#summary(semTools::compareFit(RICLPM_multi_adhd_S2.fit, RICLPM_multi_adhd_S3.fit, nested = TRUE)) #† indicates the best fitting model

RICLPM_multi_adhd_S4: Total ADHD symptoms step 4 - Full model

RICLPM_multi_adhd_S4 <- '
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for ADHD (inattention and hyperactivity) symptoms at 4 waves
  Fadhd5 =~ a*pe81m5 + b*pe82m5 + c*pe83m5 + d*pe86m5 + e*pe87m5 + f*pe88m5 + g*pe89m5 + h*pe90m5 + i*pe91m5 + j*pe84m5 + k*pe85m5 + l*pe96m5 + m*pe97m5 + n*pe92m5 + o*pe93m5 + p*pe94m5 + q*pe95m5 + r*pe64m5
  Fadhd7 =~ a*pe81m7 + b*pe82m7 + c*pe83m7 + d*pe86m7 + e*pe87m7 + f*pe88m7 + g*pe89m7 + h*pe90m7 + i*pe91m7 + j*pe84m7 + k*pe85m7 + l*pe96m7 + m*pe97m7 + n*pe92m7 + o*pe93m7 + p*pe94m7 + q*pe95m7 + r*pe64m7
  Fadhd10 =~ a*pe81m10 + b*pe82m10 + c*pe83m10 + d*pe86m10 + e*pe87m10 + f*pe88m10 + g*pe89m10 + h*pe90m10 + i*pe91m10 + j*pe84m10 + k*pe85m10 + l*pe96m10 + m*pe97m10 + n*pe92m10 + o*pe93m10 + p*pe94m10 + q*pe95m10 + r*pe64m10
  Fadhd12 =~ a*pe81m12 + b*pe82m12 + c*pe83m12 + d*pe86m12 + e*pe87m12 + f*pe88m12 + g*pe89m12 + h*pe90m12 + i*pe91m12 + j*pe84m12 + k*pe85m12 + l*pe96m12 + m*pe97m12 + n*pe92m12 + o*pe93m12 + p*pe94m12 + q*pe95m12 + r*pe64m12 
  
  # Factor models for social isolation at 4 waves
  Fsi5 =~ s*pe2m5 + t*pe4m5 + u*pe7m5 + v*pe11m5 + w*pe13m5 + x*pe25m5 
  Fsi7 =~ s*pe2m7 + t*pe4m7 + u*pe7m7 + v*pe11m7 + w*pe13m7 + x*pe25m7 
  Fsi10 =~ s*pe2m10 + t*pe4m10 + u*pe7m10 + v*pe11m10 + w*pe13m10 + x*pe25m10
  Fsi12 =~ s*pe2m12 + t*pe4m12 + u*pe7m12 + v*pe11m12 + w*pe13m12 + x*pe25m12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  # Inattention symptoms
  pe81m5 + pe81m7 + pe81m10 + pe81m12 ~ y*1
  pe82m5 + pe82m7 + pe82m10 + pe82m12 ~ z*1
  pe83m5 + pe83m7 + pe83m10 + pe83m12 ~ aa*1
  pe86m5 + pe86m7 + pe86m10 + pe86m12 ~ ab*1
  pe87m5 + pe87m7 + pe87m10 + pe87m12 ~ ac*1
  pe88m5 + pe88m7 + pe88m10 + pe88m12 ~ ad*1
  pe89m5 + pe89m7 + pe89m10 + pe89m12 ~ ae*1
  pe90m5 + pe90m7 + pe90m10 + pe90m12 ~ af*1
  pe91m5 + pe91m7 + pe91m10 + pe91m12 ~ ag*1
  # Hyperactivity symptoms
  pe84m5 + pe84m7 + pe84m10 + pe84m12 ~ ah*1
  pe85m5 + pe85m7 + pe85m10 + pe85m12 ~ ai*1
  pe96m5 + pe96m7 + pe96m10 + pe96m12 ~ aj*1
  pe97m5 + pe97m7 + pe97m10 + pe97m12 ~ ak*1
  pe92m5 + pe92m7 + pe92m10 + pe92m12 ~ al*1
  pe93m5 + pe93m7 + pe93m10 + pe93m12 ~ am*1
  pe94m5 + pe94m7 + pe94m10 + pe94m12 ~ an*1
  pe95m5 + pe95m7 + pe95m10 + pe95m12 ~ ao*1
  pe64m5 + pe64m7 + pe64m10 + pe64m12 ~ ap*1

  # Social isolation
  pe2m5 + pe2m7 + pe2m10 + pe2m12 ~ aq*1
  pe4m5 + pe4m7 + pe4m10 + pe4m12 ~ ar*1
  pe7m5 + pe7m7 + pe7m10 + pe7m12 ~ as*1
  pe11m5 + pe11m7 + pe11m10 + pe11m12 ~ at*1
  pe13m5 + pe13m7 + pe13m10 + pe13m12 ~ au*1
  pe25m5 + pe25m7 + pe25m10 + pe25m12 ~ av*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  Fadhd7 + Fadhd10 + Fadhd12 + Fsi7 + Fsi10 + Fsi12 ~ 1
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) 
  RIadhd =~ 1*Fadhd5 + 1*Fadhd7 + 1*Fadhd10 + 1*Fadhd12
  RIsi =~ 1*Fsi5 + 1*Fsi7 + 1*Fsi10 + 1*Fsi12
  
  # Set the residual variances of all Fadhd and Fsi variables to 0. 
  Fadhd5 ~~ 0*Fadhd5
  Fadhd7 ~~ 0*Fadhd7
  Fadhd10 ~~ 0*Fadhd10
  Fadhd12 ~~ 0*Fadhd12
  Fsi5 ~~ 0*Fsi5
  Fsi7 ~~ 0*Fsi7
  Fsi10 ~~ 0*Fsi10
  Fsi12 ~~ 0*Fsi12
  
  ###############
  # WITHIN PART #
  ###############
  
  # Create the within-part
  WFadhd5 =~ 1*Fadhd5
  WFadhd7 =~ 1*Fadhd7
  WFadhd10 =~ 1*Fadhd10
  WFadhd12 =~ 1*Fadhd12
  
  WFsi5 =~ 1*Fsi5
  WFsi7 =~ 1*Fsi7
  WFsi10 =~ 1*Fsi10
  WFsi12 =~ 1*Fsi12
  
  # Specify the lagged effects between the within-person centered latent variables
  WFadhd7 + WFsi7 ~ WFadhd5 + WFsi5
  WFadhd10 + WFsi10 ~ WFadhd7 + WFsi7
  WFadhd12 + WFsi12 ~ WFadhd10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFadhd5 ~~ WFsi5
  WFadhd7 ~~ WFsi7
  WFadhd10 ~~ WFsi10 
  WFadhd12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Set correlations between the between-factors (random intercepts) and within-factors at wave 1 (age 5) at 0
  RIadhd + RIsi ~~ 0*WFadhd5 + 0*WFsi5
'

RICLPM_multi_adhd_S4.fit <- cfa(RICLPM_multi_adhd_S4, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPM_multi_adhd_S4.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 158 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 409 Number of equality constraints 138

Number of observations 2232 Number of missing patterns 70

Model Test User Model: Standard Robust Test Statistic 46484.384 22407.440 Degrees of freedom 4481 4481 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.398 Shift parameter 3018.895 simple second-order correction

Model Test Baseline Model:

Test statistic 953296.763 166852.672 Degrees of freedom 4560 4560 P-value 0.000 0.000 Scaling correction factor 5.846

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.956 0.890 Tucker-Lewis Index (TLI) 0.955 0.888

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.065 0.042 90 Percent confidence interval - lower 0.064 0.042 90 Percent confidence interval - upper 0.065 0.043 P-value RMSEA <= 0.05 0.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.081 0.081

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Fadhd5 =~
pe81m5 (a) 1.000 0.821 0.821 pe82m5 (b) 0.900 0.009 102.969 0.000 0.739 0.739 pe83m5 (c) 0.976 0.008 127.274 0.000 0.801 0.801 pe86m5 (d) 0.875 0.011 80.058 0.000 0.718 0.718 pe87m5 (e) 0.706 0.017 42.495 0.000 0.580 0.580 pe88m5 (f) 0.730 0.014 52.813 0.000 0.599 0.599 pe89m5 (g) 0.780 0.013 58.339 0.000 0.640 0.640 pe90m5 (h) 0.633 0.017 36.973 0.000 0.520 0.520 pe91m5 (i) 0.813 0.015 54.175 0.000 0.667 0.667 pe84m5 (j) 0.887 0.011 82.949 0.000 0.728 0.728 pe85m5 (k) 0.806 0.013 63.863 0.000 0.661 0.661 pe96m5 (l) 0.651 0.017 38.999 0.000 0.534 0.534 pe97m5 (m) 0.803 0.013 61.837 0.000 0.659 0.659 pe92m5 (n) 0.915 0.011 85.710 0.000 0.751 0.751 pe93m5 (o) 1.001 0.009 111.442 0.000 0.821 0.821 pe94m5 (p) 0.990 0.010 101.526 0.000 0.812 0.812 pe95m5 (q) 0.815 0.014 58.772 0.000 0.669 0.669 pe64m5 (r) 0.609 0.019 31.696 0.000 0.500 0.500 Fadhd7 =~
pe81m7 (a) 1.000 0.849 0.849 pe82m7 (b) 0.900 0.009 102.969 0.000 0.765 0.765 pe83m7 (c) 0.976 0.008 127.274 0.000 0.829 0.829 pe86m7 (d) 0.875 0.011 80.058 0.000 0.743 0.743 pe87m7 (e) 0.706 0.017 42.495 0.000 0.600 0.600 pe88m7 (f) 0.730 0.014 52.813 0.000 0.620 0.620 pe89m7 (g) 0.780 0.013 58.339 0.000 0.662 0.662 pe90m7 (h) 0.633 0.017 36.973 0.000 0.538 0.538 pe91m7 (i) 0.813 0.015 54.175 0.000 0.691 0.691 pe84m7 (j) 0.887 0.011 82.949 0.000 0.754 0.754 pe85m7 (k) 0.806 0.013 63.863 0.000 0.684 0.684 pe96m7 (l) 0.651 0.017 38.999 0.000 0.553 0.553 pe97m7 (m) 0.803 0.013 61.837 0.000 0.682 0.682 pe92m7 (n) 0.915 0.011 85.710 0.000 0.777 0.777 pe93m7 (o) 1.001 0.009 111.442 0.000 0.850 0.850 pe94m7 (p) 0.990 0.010 101.526 0.000 0.841 0.841 pe95m7 (q) 0.815 0.014 58.772 0.000 0.693 0.693 pe64m7 (r) 0.609 0.019 31.696 0.000 0.517 0.517 Fadhd10 =~
pe81m10 (a) 1.000 0.862 0.862 pe82m10 (b) 0.900 0.009 102.969 0.000 0.776 0.776 pe83m10 (c) 0.976 0.008 127.274 0.000 0.842 0.842 pe86m10 (d) 0.875 0.011 80.058 0.000 0.754 0.754 pe87m10 (e) 0.706 0.017 42.495 0.000 0.609 0.609 pe88m10 (f) 0.730 0.014 52.813 0.000 0.629 0.629 pe89m10 (g) 0.780 0.013 58.339 0.000 0.672 0.672 pe90m10 (h) 0.633 0.017 36.973 0.000 0.546 0.546 pe91m10 (i) 0.813 0.015 54.175 0.000 0.701 0.701 pe84m10 (j) 0.887 0.011 82.949 0.000 0.765 0.765 pe85m10 (k) 0.806 0.013 63.863 0.000 0.695 0.695 pe96m10 (l) 0.651 0.017 38.999 0.000 0.561 0.561 pe97m10 (m) 0.803 0.013 61.837 0.000 0.692 0.692 pe92m10 (n) 0.915 0.011 85.710 0.000 0.789 0.789 pe93m10 (o) 1.001 0.009 111.442 0.000 0.862 0.862 pe94m10 (p) 0.990 0.010 101.526 0.000 0.853 0.853 pe95m10 (q) 0.815 0.014 58.772 0.000 0.703 0.703 pe64m10 (r) 0.609 0.019 31.696 0.000 0.525 0.525 Fadhd12 =~
pe81m12 (a) 1.000 0.900 0.900 pe82m12 (b) 0.900 0.009 102.969 0.000 0.810 0.810 pe83m12 (c) 0.976 0.008 127.274 0.000 0.878 0.878 pe86m12 (d) 0.875 0.011 80.058 0.000 0.787 0.787 pe87m12 (e) 0.706 0.017 42.495 0.000 0.635 0.635 pe88m12 (f) 0.730 0.014 52.813 0.000 0.657 0.657 pe89m12 (g) 0.780 0.013 58.339 0.000 0.702 0.702 pe90m12 (h) 0.633 0.017 36.973 0.000 0.570 0.570 pe91m12 (i) 0.813 0.015 54.175 0.000 0.732 0.732 pe84m12 (j) 0.887 0.011 82.949 0.000 0.798 0.798 pe85m12 (k) 0.806 0.013 63.863 0.000 0.725 0.725 pe96m12 (l) 0.651 0.017 38.999 0.000 0.586 0.586 pe97m12 (m) 0.803 0.013 61.837 0.000 0.722 0.722 pe92m12 (n) 0.915 0.011 85.710 0.000 0.823 0.823 pe93m12 (o) 1.001 0.009 111.442 0.000 0.900 0.900 pe94m12 (p) 0.990 0.010 101.526 0.000 0.891 0.891 pe95m12 (q) 0.815 0.014 58.772 0.000 0.734 0.734 pe64m12 (r) 0.609 0.019 31.696 0.000 0.548 0.548 Fsi5 =~
pe2m5 (s) 1.000 0.587 0.587 pe4m5 (t) 1.386 0.056 24.809 0.000 0.814 0.814 pe7m5 (u) 0.998 0.043 23.193 0.000 0.586 0.586 pe11m5 (v) 0.799 0.048 16.776 0.000 0.469 0.469 pe13m5 (w) 1.486 0.060 24.583 0.000 0.873 0.873 pe25m5 (x) 1.251 0.057 22.086 0.000 0.735 0.735 Fsi7 =~
pe2m7 (s) 1.000 0.626 0.626 pe4m7 (t) 1.386 0.056 24.809 0.000 0.867 0.867 pe7m7 (u) 0.998 0.043 23.193 0.000 0.624 0.624 pe11m7 (v) 0.799 0.048 16.776 0.000 0.500 0.500 pe13m7 (w) 1.486 0.060 24.583 0.000 0.930 0.930 pe25m7 (x) 1.251 0.057 22.086 0.000 0.783 0.783 Fsi10 =~
pe2m10 (s) 1.000 0.623 0.623 pe4m10 (t) 1.386 0.056 24.809 0.000 0.864 0.864 pe7m10 (u) 0.998 0.043 23.193 0.000 0.622 0.622 pe11m10 (v) 0.799 0.048 16.776 0.000 0.498 0.498 pe13m10 (w) 1.486 0.060 24.583 0.000 0.926 0.926 pe25m10 (x) 1.251 0.057 22.086 0.000 0.780 0.780 Fsi12 =~
pe2m12 (s) 1.000 0.643 0.643 pe4m12 (t) 1.386 0.056 24.809 0.000 0.892 0.892 pe7m12 (u) 0.998 0.043 23.193 0.000 0.642 0.642 pe11m12 (v) 0.799 0.048 16.776 0.000 0.514 0.514 pe13m12 (w) 1.486 0.060 24.583 0.000 0.956 0.956 pe25m12 (x) 1.251 0.057 22.086 0.000 0.805 0.805 RIadhd =~
Fadhd5 1.000 0.747 0.747 Fadhd7 1.000 0.722 0.722 Fadhd10 1.000 0.711 0.711 Fadhd12 1.000 0.682 0.682 RIsi =~
Fsi5 1.000 NA NA Fsi7 1.000 NA NA Fsi10 1.000 NA NA Fsi12 1.000 NA NA WFadhd5 =~
Fadhd5 1.000 0.664 0.664 WFadhd7 =~
Fadhd7 1.000 0.692 0.692 WFadhd10 =~
Fadhd10 1.000 0.703 0.703 WFadhd12 =~
Fadhd12 1.000 0.732 0.732 WFsi5 =~
Fsi5 1.000 1.286 1.286 WFsi7 =~
Fsi7 1.000 1.255 1.255 WFsi10 =~
Fsi10 1.000 1.257 1.257 WFsi12 =~
Fsi12 1.000 1.243 1.243

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd7 ~
WFadhd5 0.481 0.059 8.150 0.000 0.447 0.447 WFsi5 -0.122 0.081 -1.513 0.130 -0.157 -0.157 WFsi7 ~
WFadhd5 -0.100 0.063 -1.587 0.112 -0.070 -0.070 WFsi5 0.900 0.073 12.285 0.000 0.865 0.865 WFadhd10 ~
WFadhd7 0.501 0.056 8.920 0.000 0.486 0.486 WFsi7 -0.085 0.085 -1.003 0.316 -0.110 -0.110 WFsi10 ~
WFadhd7 -0.090 0.067 -1.350 0.177 -0.068 -0.068 WFsi7 0.845 0.110 7.691 0.000 0.847 0.847 WFadhd12 ~
WFadhd10 0.808 0.029 28.095 0.000 0.744 0.744 WFsi10 -0.060 0.037 -1.615 0.106 -0.071 -0.071 WFsi12 ~
WFadhd10 0.015 0.039 0.372 0.710 0.011 0.011 WFsi10 0.941 0.053 17.662 0.000 0.923 0.923

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFadhd5 ~~
WFsi5 -0.050 0.139 -0.359 0.720 -0.122 -0.122 .WFadhd7 ~~
.WFsi7 0.087 0.013 6.521 0.000 0.448 0.448 .WFadhd10 ~~
.WFsi10 0.060 0.012 5.188 0.000 0.280 0.280 .WFadhd12 ~~
.WFsi12 0.072 0.009 7.691 0.000 0.532 0.532 RIadhd ~~
WFadhd5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIsi ~~
WFadhd5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIadhd ~~
RIsi 0.272 0.139 1.958 0.050 0.934 0.934

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .pe81m5 (y) -0.022 0.010 -2.215 0.027 -0.022 -0.022 .pe81m7 (y) -0.022 0.010 -2.215 0.027 -0.022 -0.022 .pe81m10 (y) -0.022 0.010 -2.215 0.027 -0.022 -0.022 .pe81m12 (y) -0.022 0.010 -2.215 0.027 -0.022 -0.022 .pe82m5 (z) -0.001 0.010 -0.094 0.925 -0.001 -0.001 .pe82m7 (z) -0.001 0.010 -0.094 0.925 -0.001 -0.001 .pe82m10 (z) -0.001 0.010 -0.094 0.925 -0.001 -0.001 .pe82m12 (z) -0.001 0.010 -0.094 0.925 -0.001 -0.001 .pe83m5 (aa) 0.000 0.009 0.021 0.983 0.000 0.000 .pe83m7 (aa) 0.000 0.009 0.021 0.983 0.000 0.000 .pe83m10 (aa) 0.000 0.009 0.021 0.983 0.000 0.000 .pe83m12 (aa) 0.000 0.009 0.021 0.983 0.000 0.000 .pe86m5 (ab) 0.001 0.010 0.112 0.911 0.001 0.001 .pe86m7 (ab) 0.001 0.010 0.112 0.911 0.001 0.001 .pe86m10 (ab) 0.001 0.010 0.112 0.911 0.001 0.001 .pe86m12 (ab) 0.001 0.010 0.112 0.911 0.001 0.001 .pe87m5 (ac) -0.024 0.012 -2.033 0.042 -0.024 -0.024 .pe87m7 (ac) -0.024 0.012 -2.033 0.042 -0.024 -0.024 .pe87m10 (ac) -0.024 0.012 -2.033 0.042 -0.024 -0.024 .pe87m12 (ac) -0.024 0.012 -2.033 0.042 -0.024 -0.024 .pe88m5 (ad) 0.009 0.010 0.893 0.372 0.009 0.009 .pe88m7 (ad) 0.009 0.010 0.893 0.372 0.009 0.009 .pe88m10 (ad) 0.009 0.010 0.893 0.372 0.009 0.009 .pe88m12 (ad) 0.009 0.010 0.893 0.372 0.009 0.009 .pe89m5 (ae) -0.040 0.011 -3.626 0.000 -0.040 -0.040 .pe89m7 (ae) -0.040 0.011 -3.626 0.000 -0.040 -0.040 .pe89m10 (ae) -0.040 0.011 -3.626 0.000 -0.040 -0.040 .pe89m12 (ae) -0.040 0.011 -3.626 0.000 -0.040 -0.040 .pe90m5 (af) -0.007 0.011 -0.621 0.535 -0.007 -0.007 .pe90m7 (af) -0.007 0.011 -0.621 0.535 -0.007 -0.007 .pe90m10 (af) -0.007 0.011 -0.621 0.535 -0.007 -0.007 .pe90m12 (af) -0.007 0.011 -0.621 0.535 -0.007 -0.007 .pe91m5 (ag) 0.006 0.012 0.519 0.603 0.006 0.006 .pe91m7 (ag) 0.006 0.012 0.519 0.603 0.006 0.006 .pe91m10 (ag) 0.006 0.012 0.519 0.603 0.006 0.006 .pe91m12 (ag) 0.006 0.012 0.519 0.603 0.006 0.006 .pe84m5 (ah) 0.005 0.010 0.506 0.613 0.005 0.005 .pe84m7 (ah) 0.005 0.010 0.506 0.613 0.005 0.005 .pe84m10 (ah) 0.005 0.010 0.506 0.613 0.005 0.005 .pe84m12 (ah) 0.005 0.010 0.506 0.613 0.005 0.005 .pe85m5 (ai) -0.005 0.010 -0.544 0.586 -0.005 -0.005 .pe85m7 (ai) -0.005 0.010 -0.544 0.586 -0.005 -0.005 .pe85m10 (ai) -0.005 0.010 -0.544 0.586 -0.005 -0.005 .pe85m12 (ai) -0.005 0.010 -0.544 0.586 -0.005 -0.005 .pe96m5 (aj) 0.002 0.011 0.174 0.862 0.002 0.002 .pe96m7 (aj) 0.002 0.011 0.174 0.862 0.002 0.002 .pe96m10 (aj) 0.002 0.011 0.174 0.862 0.002 0.002 .pe96m12 (aj) 0.002 0.011 0.174 0.862 0.002 0.002 .pe97m5 (ak) -0.015 0.010 -1.465 0.143 -0.015 -0.015 .pe97m7 (ak) -0.015 0.010 -1.465 0.143 -0.015 -0.015 .pe97m10 (ak) -0.015 0.010 -1.465 0.143 -0.015 -0.015 .pe97m12 (ak) -0.015 0.010 -1.465 0.143 -0.015 -0.015 .pe92m5 (al) -0.000 0.010 -0.031 0.976 -0.000 -0.000 .pe92m7 (al) -0.000 0.010 -0.031 0.976 -0.000 -0.000 .pe92m10 (al) -0.000 0.010 -0.031 0.976 -0.000 -0.000 .pe92m12 (al) -0.000 0.010 -0.031 0.976 -0.000 -0.000 .pe93m5 (am) -0.024 0.010 -2.386 0.017 -0.024 -0.024 .pe93m7 (am) -0.024 0.010 -2.386 0.017 -0.024 -0.024 .pe93m10 (am) -0.024 0.010 -2.386 0.017 -0.024 -0.024 .pe93m12 (am) -0.024 0.010 -2.386 0.017 -0.024 -0.024 .pe94m5 (an) 0.010 0.011 0.856 0.392 0.010 0.010 .pe94m7 (an) 0.010 0.011 0.856 0.392 0.010 0.010 .pe94m10 (an) 0.010 0.011 0.856 0.392 0.010 0.010 .pe94m12 (an) 0.010 0.011 0.856 0.392 0.010 0.010 .pe95m5 (ao) 0.032 0.011 2.940 0.003 0.032 0.032 .pe95m7 (ao) 0.032 0.011 2.940 0.003 0.032 0.032 .pe95m10 (ao) 0.032 0.011 2.940 0.003 0.032 0.032 .pe95m12 (ao) 0.032 0.011 2.940 0.003 0.032 0.032 .pe64m5 (ap) 0.023 0.012 1.916 0.055 0.023 0.023 .pe64m7 (ap) 0.023 0.012 1.916 0.055 0.023 0.023 .pe64m10 (ap) 0.023 0.012 1.916 0.055 0.023 0.023 .pe64m12 (ap) 0.023 0.012 1.916 0.055 0.023 0.023 .pe2m5 (aq) -0.001 0.016 -0.055 0.956 -0.001 -0.001 .pe2m7 (aq) -0.001 0.016 -0.055 0.956 -0.001 -0.001 .pe2m10 (aq) -0.001 0.016 -0.055 0.956 -0.001 -0.001 .pe2m12 (aq) -0.001 0.016 -0.055 0.956 -0.001 -0.001 .pe4m5 (ar) -0.013 0.014 -0.887 0.375 -0.013 -0.013 .pe4m7 (ar) -0.013 0.014 -0.887 0.375 -0.013 -0.013 .pe4m10 (ar) -0.013 0.014 -0.887 0.375 -0.013 -0.013 .pe4m12 (ar) -0.013 0.014 -0.887 0.375 -0.013 -0.013 .pe7m5 (as) -0.001 0.014 -0.079 0.937 -0.001 -0.001 .pe7m7 (as) -0.001 0.014 -0.079 0.937 -0.001 -0.001 .pe7m10 (as) -0.001 0.014 -0.079 0.937 -0.001 -0.001 .pe7m12 (as) -0.001 0.014 -0.079 0.937 -0.001 -0.001 .pe11m5 (at) -0.011 0.015 -0.725 0.469 -0.011 -0.011 .pe11m7 (at) -0.011 0.015 -0.725 0.469 -0.011 -0.011 .pe11m10 (at) -0.011 0.015 -0.725 0.469 -0.011 -0.011 .pe11m12 (at) -0.011 0.015 -0.725 0.469 -0.011 -0.011 .pe13m5 (au) 0.008 0.020 0.391 0.696 0.008 0.008 .pe13m7 (au) 0.008 0.020 0.391 0.696 0.008 0.008 .pe13m10 (au) 0.008 0.020 0.391 0.696 0.008 0.008 .pe13m12 (au) 0.008 0.020 0.391 0.696 0.008 0.008 .pe25m5 (av) -0.001 0.018 -0.058 0.954 -0.001 -0.001 .pe25m7 (av) -0.001 0.018 -0.058 0.954 -0.001 -0.001 .pe25m10 (av) -0.001 0.018 -0.058 0.954 -0.001 -0.001 .pe25m12 (av) -0.001 0.018 -0.058 0.954 -0.001 -0.001 .Fadhd7 0.003 0.016 0.220 0.826 0.004 0.004 .Fadhd10 0.003 0.017 0.172 0.863 0.003 0.003 .Fadhd12 0.003 0.018 0.169 0.866 0.003 0.003 .Fsi7 0.003 0.022 0.139 0.890 0.005 0.005 .Fsi10 0.003 0.021 0.122 0.903 0.004 0.004 .Fsi12 0.002 0.023 0.108 0.914 0.004 0.004 .Fadhd5 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 RIadhd 0.000 0.000 0.000 RIsi 0.000 NA NA WFadhd5 0.000 0.000 0.000 .WFadhd7 0.000 0.000 0.000 .WFadhd10 0.000 0.000 0.000 .WFadhd12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5|t1 0.237 0.022 10.905 0.000 0.237 0.237 pe81m5|t2 1.075 0.027 39.251 0.000 1.075 1.075 pe82m5|t1 0.097 0.022 4.410 0.000 0.097 0.097 pe82m5|t2 1.069 0.027 39.214 0.000 1.069 1.069 pe83m5|t1 -0.110 0.022 -5.092 0.000 -0.110 -0.110 pe83m5|t2 0.763 0.024 31.483 0.000 0.763 0.763 pe86m5|t1 0.063 0.022 2.840 0.005 0.063 0.063 pe86m5|t2 1.141 0.029 39.896 0.000 1.141 1.141 pe87m5|t1 0.291 0.023 12.611 0.000 0.291 0.291 pe87m5|t2 1.472 0.035 41.573 0.000 1.472 1.472 pe88m5|t1 0.412 0.023 18.094 0.000 0.412 0.412 pe88m5|t2 1.147 0.029 39.607 0.000 1.147 1.147 pe89m5|t1 0.439 0.023 18.976 0.000 0.439 0.439 pe89m5|t2 1.250 0.031 40.518 0.000 1.250 1.250 pe90m5|t1 0.048 0.022 2.209 0.027 0.048 0.048 pe90m5|t2 0.800 0.025 32.342 0.000 0.800 0.800 pe91m5|t1 0.585 0.023 25.108 0.000 0.585 0.585 pe91m5|t2 1.449 0.033 43.508 0.000 1.449 1.449 pe84m5|t1 0.003 0.022 0.150 0.881 0.003 0.003 pe84m5|t2 0.977 0.026 37.318 0.000 0.977 0.977 pe85m5|t1 -0.937 0.026 -35.404 0.000 -0.937 -0.937 pe85m5|t2 0.261 0.022 11.633 0.000 0.261 0.261 pe96m5|t1 -0.186 0.022 -8.420 0.000 -0.186 -0.186 pe96m5|t2 0.851 0.025 33.459 0.000 0.851 0.851 pe97m5|t1 -0.372 0.023 -16.463 0.000 -0.372 -0.372 pe97m5|t2 0.591 0.024 25.108 0.000 0.591 0.591 pe92m5|t1 0.004 0.021 0.197 0.844 0.004 0.004 pe92m5|t2 0.809 0.024 33.247 0.000 0.809 0.809 pe93m5|t1 0.174 0.021 8.110 0.000 0.174 0.174 pe93m5|t2 0.850 0.024 34.968 0.000 0.850 0.850 pe94m5|t1 0.593 0.023 26.173 0.000 0.593 0.593 pe94m5|t2 1.239 0.028 43.563 0.000 1.239 1.239 pe95m5|t1 0.026 0.021 1.244 0.213 0.026 0.026 pe95m5|t2 0.541 0.022 24.476 0.000 0.541 0.541 pe64m5|t1 -0.223 0.021 -10.571 0.000 -0.223 -0.223 pe64m5|t2 0.513 0.022 23.459 0.000 0.513 0.513 pe81m7|t1 0.287 0.019 14.781 0.000 0.287 0.287 pe81m7|t2 1.164 0.024 49.259 0.000 1.164 1.164 pe82m7|t1 0.196 0.021 9.504 0.000 0.196 0.196 pe82m7|t2 1.260 0.026 48.444 0.000 1.260 1.260 pe83m7|t1 -0.021 0.020 -1.078 0.281 -0.021 -0.021 pe83m7|t2 0.966 0.021 45.144 0.000 0.966 0.966 pe86m7|t1 0.151 0.021 7.331 0.000 0.151 0.151 pe86m7|t2 1.311 0.027 48.045 0.000 1.311 1.311 pe87m7|t1 0.245 0.022 10.973 0.000 0.245 0.245 pe87m7|t2 1.617 0.037 43.480 0.000 1.617 1.617 pe88m7|t1 0.339 0.021 15.966 0.000 0.339 0.339 pe88m7|t2 1.294 0.028 45.544 0.000 1.294 1.294 pe89m7|t1 0.461 0.021 21.800 0.000 0.461 0.461 pe89m7|t2 1.371 0.031 44.861 0.000 1.371 1.371 pe90m7|t1 0.040 0.021 1.909 0.056 0.040 0.040 pe90m7|t2 0.983 0.024 40.479 0.000 0.983 0.983 pe91m7|t1 0.579 0.021 26.980 0.000 0.579 0.579 pe91m7|t2 1.543 0.032 48.974 0.000 1.543 1.543 pe84m7|t1 0.085 0.021 4.118 0.000 0.085 0.085 pe84m7|t2 1.240 0.026 47.815 0.000 1.240 1.240 pe85m7|t1 -0.868 0.026 -33.407 0.000 -0.868 -0.868 pe85m7|t2 0.584 0.021 28.077 0.000 0.584 0.584 pe96m7|t1 -0.165 0.022 -7.545 0.000 -0.165 -0.165 pe96m7|t2 0.927 0.024 38.192 0.000 0.927 0.927 pe97m7|t1 -0.280 0.022 -12.788 0.000 -0.280 -0.280 pe97m7|t2 0.780 0.022 35.531 0.000 0.780 0.780 pe92m7|t1 0.062 0.020 3.125 0.002 0.062 0.062 pe92m7|t2 0.962 0.022 44.292 0.000 0.962 0.962 pe93m7|t1 0.335 0.019 17.816 0.000 0.335 0.335 pe93m7|t2 0.982 0.021 47.514 0.000 0.982 0.982 pe94m7|t1 0.731 0.020 37.384 0.000 0.731 0.731 pe94m7|t2 1.388 0.026 52.730 0.000 1.388 1.388 pe95m7|t1 0.184 0.020 9.404 0.000 0.184 0.184 pe95m7|t2 0.802 0.020 39.240 0.000 0.802 0.802 pe64m7|t1 -0.258 0.021 -12.105 0.000 -0.258 -0.258 pe64m7|t2 0.660 0.022 30.041 0.000 0.660 0.660 pe81m10|t1 0.373 0.019 19.404 0.000 0.373 0.373 pe81m10|t2 1.196 0.024 50.061 0.000 1.196 1.196 pe82m10|t1 0.228 0.021 10.784 0.000 0.228 0.228 pe82m10|t2 1.329 0.027 49.880 0.000 1.329 1.329 pe83m10|t1 -0.022 0.020 -1.107 0.268 -0.022 -0.022 pe83m10|t2 1.015 0.022 46.741 0.000 1.015 1.015 pe86m10|t1 0.158 0.021 7.593 0.000 0.158 0.158 pe86m10|t2 1.289 0.027 47.286 0.000 1.289 1.289 pe87m10|t1 0.383 0.023 16.833 0.000 0.383 0.383 pe87m10|t2 1.742 0.040 43.246 0.000 1.742 1.742 pe88m10|t1 0.067 0.021 3.164 0.002 0.067 0.067 pe88m10|t2 1.025 0.024 42.225 0.000 1.025 1.025 pe89m10|t1 0.426 0.022 19.759 0.000 0.426 0.426 pe89m10|t2 1.361 0.030 44.918 0.000 1.361 1.361 pe90m10|t1 0.036 0.021 1.738 0.082 0.036 0.036 pe90m10|t2 0.934 0.024 38.930 0.000 0.934 0.934 pe91m10|t1 0.656 0.022 29.518 0.000 0.656 0.656 pe91m10|t2 1.613 0.034 47.032 0.000 1.613 1.613 pe84m10|t1 0.120 0.021 5.800 0.000 0.120 0.120 pe84m10|t2 1.265 0.026 48.357 0.000 1.265 1.265 pe85m10|t1 -0.483 0.022 -21.837 0.000 -0.483 -0.483 pe85m10|t2 0.862 0.023 37.713 0.000 0.862 0.862 pe96m10|t1 -0.033 0.021 -1.573 0.116 -0.033 -0.033 pe96m10|t2 1.076 0.026 41.999 0.000 1.076 1.076 pe97m10|t1 0.127 0.021 6.165 0.000 0.127 0.127 pe97m10|t2 1.098 0.025 44.094 0.000 1.098 1.098 pe92m10|t1 0.369 0.019 19.372 0.000 0.369 0.369 pe92m10|t2 1.155 0.023 50.134 0.000 1.155 1.155 pe93m10|t1 0.495 0.018 26.892 0.000 0.495 0.495 pe93m10|t2 1.218 0.024 51.171 0.000 1.218 1.218 pe94m10|t1 0.827 0.020 40.378 0.000 0.827 0.827 pe94m10|t2 1.569 0.031 50.134 0.000 1.569 1.569 pe95m10|t1 0.322 0.020 16.506 0.000 0.322 0.322 pe95m10|t2 0.975 0.023 43.319 0.000 0.975 0.975 pe64m10|t1 -0.050 0.020 -2.453 0.014 -0.050 -0.050 pe64m10|t2 0.894 0.023 38.218 0.000 0.894 0.894 pe81m12|t1 0.408 0.019 21.318 0.000 0.408 0.408 pe81m12|t2 1.232 0.024 51.529 0.000 1.232 1.232 pe82m12|t1 0.309 0.020 15.124 0.000 0.309 0.309 pe82m12|t2 1.371 0.027 50.414 0.000 1.371 1.371 pe83m12|t1 0.054 0.020 2.674 0.007 0.054 0.054 pe83m12|t2 1.100 0.022 49.320 0.000 1.100 1.100 pe86m12|t1 0.083 0.021 3.916 0.000 0.083 0.083 pe86m12|t2 1.344 0.028 48.379 0.000 1.344 1.344 pe87m12|t1 0.344 0.022 15.583 0.000 0.344 0.344 pe87m12|t2 1.675 0.038 43.730 0.000 1.675 1.675 pe88m12|t1 0.021 0.022 0.979 0.327 0.021 0.021 pe88m12|t2 0.954 0.024 40.570 0.000 0.954 0.954 pe89m12|t1 0.423 0.022 19.425 0.000 0.423 0.423 pe89m12|t2 1.346 0.030 45.486 0.000 1.346 1.346 pe90m12|t1 0.073 0.021 3.494 0.000 0.073 0.073 pe90m12|t2 0.964 0.024 39.840 0.000 0.964 0.964 pe91m12|t1 0.581 0.022 27.001 0.000 0.581 0.581 pe91m12|t2 1.631 0.035 46.711 0.000 1.631 1.631 pe84m12|t1 0.099 0.021 4.746 0.000 0.099 0.099 pe84m12|t2 1.303 0.026 49.493 0.000 1.303 1.303 pe85m12|t1 -0.292 0.022 -13.545 0.000 -0.292 -0.292 pe85m12|t2 0.948 0.023 40.672 0.000 0.948 0.948 pe96m12|t1 0.060 0.021 2.888 0.004 0.060 0.060 pe96m12|t2 1.121 0.025 44.706 0.000 1.121 1.121 pe97m12|t1 0.244 0.020 12.034 0.000 0.244 0.244 pe97m12|t2 1.157 0.025 46.376 0.000 1.157 1.157 pe92m12|t1 0.407 0.019 20.911 0.000 0.407 0.407 pe92m12|t2 1.250 0.025 50.151 0.000 1.250 1.250 pe93m12|t1 0.576 0.019 29.932 0.000 0.576 0.576 pe93m12|t2 1.277 0.025 51.649 0.000 1.277 1.277 pe94m12|t1 0.902 0.020 44.145 0.000 0.902 0.902 pe94m12|t2 1.601 0.031 51.072 0.000 1.601 1.601 pe95m12|t1 0.500 0.019 25.941 0.000 0.500 0.500 pe95m12|t2 1.114 0.024 45.717 0.000 1.114 1.114 pe64m12|t1 -0.022 0.020 -1.085 0.278 -0.022 -0.022 pe64m12|t2 0.896 0.023 38.586 0.000 0.896 0.896 pe2m5|t1 1.364 0.033 41.178 0.000 1.364 1.364 pe2m5|t2 2.366 0.074 31.921 0.000 2.366 2.366 pe4m5|t1 0.995 0.027 36.879 0.000 0.995 0.995 pe4m5|t2 2.117 0.057 37.321 0.000 2.117 2.117 pe7m5|t1 0.832 0.026 31.890 0.000 0.832 0.832 pe7m5|t2 1.957 0.050 39.372 0.000 1.957 1.957 pe11m5|t1 0.678 0.025 27.377 0.000 0.678 0.678 pe11m5|t2 1.773 0.042 42.484 0.000 1.773 1.773 pe13m5|t1 1.584 0.036 43.437 0.000 1.584 1.584 pe13m5|t2 2.656 0.099 26.952 0.000 2.656 2.656 pe25m5|t1 1.171 0.030 39.052 0.000 1.171 1.171 pe25m5|t2 2.282 0.066 34.768 0.000 2.282 2.282 pe2m7|t1 1.178 0.032 36.527 0.000 1.178 1.178 pe2m7|t2 2.261 0.059 38.198 0.000 2.261 2.261 pe4m7|t1 1.004 0.033 30.764 0.000 1.004 1.004 pe4m7|t2 2.251 0.053 42.151 0.000 2.251 2.251 pe7m7|t1 0.663 0.027 24.156 0.000 0.663 0.663 pe7m7|t2 1.833 0.042 43.660 0.000 1.833 1.833 pe11m7|t1 0.850 0.027 31.624 0.000 0.850 0.850 pe11m7|t2 1.881 0.042 45.040 0.000 1.881 1.881 pe13m7|t1 1.469 0.038 38.400 0.000 1.469 1.469 pe13m7|t2 2.618 0.080 32.772 0.000 2.618 2.618 pe25m7|t1 1.387 0.035 39.485 0.000 1.387 1.387 pe25m7|t2 2.325 0.060 38.829 0.000 2.325 2.325 pe2m10|t1 1.008 0.030 33.417 0.000 1.008 1.008 pe2m10|t2 2.137 0.054 39.817 0.000 2.137 2.137 pe4m10|t1 0.908 0.031 29.386 0.000 0.908 0.908 pe4m10|t2 2.138 0.046 46.413 0.000 2.138 2.138 pe7m10|t1 0.651 0.027 24.201 0.000 0.651 0.651 pe7m10|t2 1.941 0.045 42.776 0.000 1.941 1.941 pe11m10|t1 0.786 0.026 29.867 0.000 0.786 0.786 pe11m10|t2 1.971 0.046 42.665 0.000 1.971 1.971 pe13m10|t1 1.264 0.036 35.241 0.000 1.264 1.264 pe13m10|t2 2.402 0.060 39.823 0.000 2.402 2.402 pe25m10|t1 1.371 0.032 43.510 0.000 1.371 1.371 pe25m10|t2 2.335 0.062 37.497 0.000 2.335 2.335 pe2m12|t1 1.131 0.030 37.228 0.000 1.131 1.131 pe2m12|t2 2.240 0.059 38.200 0.000 2.240 2.240 pe4m12|t1 0.893 0.033 27.077 0.000 0.893 0.893 pe4m12|t2 2.215 0.050 43.906 0.000 2.215 2.215 pe7m12|t1 0.718 0.027 26.807 0.000 0.718 0.718 pe7m12|t2 2.007 0.049 41.160 0.000 2.007 2.007 pe11m12|t1 0.855 0.027 31.624 0.000 0.855 0.855 pe11m12|t2 1.988 0.047 42.209 0.000 1.988 1.988 pe13m12|t1 1.209 0.036 33.616 0.000 1.209 1.209 pe13m12|t2 2.294 0.054 42.244 0.000 2.294 2.294 pe25m12|t1 1.258 0.033 38.519 0.000 1.258 1.258 pe25m12|t2 2.459 0.073 33.690 0.000 2.459 2.459

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Fadhd5 0.000 0.000 0.000 .Fadhd7 0.000 0.000 0.000 .Fadhd10 0.000 0.000 0.000 .Fadhd12 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 .Fsi7 0.000 0.000 0.000 .Fsi10 0.000 0.000 0.000 .Fsi12 0.000 0.000 0.000 .pe81m5 0.327 0.327 0.327 .pe82m5 0.454 0.454 0.454 .pe83m5 0.358 0.358 0.358 .pe86m5 0.485 0.485 0.485 .pe87m5 0.664 0.664 0.664 .pe88m5 0.641 0.641 0.641 .pe89m5 0.591 0.591 0.591 .pe90m5 0.730 0.730 0.730 .pe91m5 0.555 0.555 0.555 .pe84m5 0.470 0.470 0.470 .pe85m5 0.563 0.563 0.563 .pe96m5 0.715 0.715 0.715 .pe97m5 0.566 0.566 0.566 .pe92m5 0.436 0.436 0.436 .pe93m5 0.326 0.326 0.326 .pe94m5 0.340 0.340 0.340 .pe95m5 0.552 0.552 0.552 .pe64m5 0.750 0.750 0.750 .pe81m7 0.279 0.279 0.279 .pe82m7 0.415 0.415 0.415 .pe83m7 0.312 0.312 0.312 .pe86m7 0.448 0.448 0.448 .pe87m7 0.640 0.640 0.640 .pe88m7 0.616 0.616 0.616 .pe89m7 0.561 0.561 0.561 .pe90m7 0.711 0.711 0.711 .pe91m7 0.523 0.523 0.523 .pe84m7 0.432 0.432 0.432 .pe85m7 0.532 0.532 0.532 .pe96m7 0.694 0.694 0.694 .pe97m7 0.535 0.535 0.535 .pe92m7 0.396 0.396 0.396 .pe93m7 0.278 0.278 0.278 .pe94m7 0.293 0.293 0.293 .pe95m7 0.520 0.520 0.520 .pe64m7 0.732 0.732 0.732 .pe81m10 0.257 0.257 0.257 .pe82m10 0.398 0.398 0.398 .pe83m10 0.292 0.292 0.292 .pe86m10 0.431 0.431 0.431 .pe87m10 0.629 0.629 0.629 .pe88m10 0.604 0.604 0.604 .pe89m10 0.548 0.548 0.548 .pe90m10 0.702 0.702 0.702 .pe91m10 0.509 0.509 0.509 .pe84m10 0.415 0.415 0.415 .pe85m10 0.517 0.517 0.517 .pe96m10 0.685 0.685 0.685 .pe97m10 0.521 0.521 0.521 .pe92m10 0.378 0.378 0.378 .pe93m10 0.256 0.256 0.256 .pe94m10 0.272 0.272 0.272 .pe95m10 0.506 0.506 0.506 .pe64m10 0.724 0.724 0.724 .pe81m12 0.191 0.191 0.191 .pe82m12 0.344 0.344 0.344 .pe83m12 0.228 0.228 0.228 .pe86m12 0.381 0.381 0.381 .pe87m12 0.596 0.596 0.596 .pe88m12 0.569 0.569 0.569 .pe89m12 0.508 0.508 0.508 .pe90m12 0.675 0.675 0.675 .pe91m12 0.465 0.465 0.465 .pe84m12 0.363 0.363 0.363 .pe85m12 0.474 0.474 0.474 .pe96m12 0.657 0.657 0.657 .pe97m12 0.478 0.478 0.478 .pe92m12 0.323 0.323 0.323 .pe93m12 0.190 0.190 0.190 .pe94m12 0.207 0.207 0.207 .pe95m12 0.462 0.462 0.462 .pe64m12 0.700 0.700 0.700 .pe2m5 0.655 0.655 0.655 .pe4m5 0.337 0.337 0.337 .pe7m5 0.656 0.656 0.656 .pe11m5 0.780 0.780 0.780 .pe13m5 0.238 0.238 0.238 .pe25m5 0.460 0.460 0.460 .pe2m7 0.609 0.609 0.609 .pe4m7 0.248 0.248 0.248 .pe7m7 0.610 0.610 0.610 .pe11m7 0.750 0.750 0.750 .pe13m7 0.136 0.136 0.136 .pe25m7 0.387 0.387 0.387 .pe2m10 0.611 0.611 0.611 .pe4m10 0.253 0.253 0.253 .pe7m10 0.613 0.613 0.613 .pe11m10 0.752 0.752 0.752 .pe13m10 0.142 0.142 0.142 .pe25m10 0.391 0.391 0.391 .pe2m12 0.586 0.586 0.586 .pe4m12 0.205 0.205 0.205 .pe7m12 0.588 0.588 0.588 .pe11m12 0.736 0.736 0.736 .pe13m12 0.086 0.086 0.086 .pe25m12 0.352 0.352 0.352 RIadhd 0.376 0.051 7.326 0.000 1.000 1.000 RIsi -0.225 0.470 -0.480 0.631 NA NA WFadhd5 0.297 0.051 5.775 0.000 1.000 1.000 .WFadhd7 0.262 0.013 20.467 0.000 0.759 0.759 .WFadhd10 0.275 0.012 22.408 0.000 0.748 0.748 .WFadhd12 0.191 0.011 17.767 0.000 0.441 0.441 WFsi5 0.571 0.471 1.211 0.226 1.000 1.000 .WFsi7 0.143 0.019 7.460 0.000 0.232 0.232 .WFsi10 0.169 0.018 9.240 0.000 0.275 0.275 .WFsi12 0.095 0.014 6.930 0.000 0.149 0.149

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all pe81m5 1.000 1.000 1.000 pe82m5 1.000 1.000 1.000 pe83m5 1.000 1.000 1.000 pe86m5 1.000 1.000 1.000 pe87m5 1.000 1.000 1.000 pe88m5 1.000 1.000 1.000 pe89m5 1.000 1.000 1.000 pe90m5 1.000 1.000 1.000 pe91m5 1.000 1.000 1.000 pe84m5 1.000 1.000 1.000 pe85m5 1.000 1.000 1.000 pe96m5 1.000 1.000 1.000 pe97m5 1.000 1.000 1.000 pe92m5 1.000 1.000 1.000 pe93m5 1.000 1.000 1.000 pe94m5 1.000 1.000 1.000 pe95m5 1.000 1.000 1.000 pe64m5 1.000 1.000 1.000 pe81m7 1.000 1.000 1.000 pe82m7 1.000 1.000 1.000 pe83m7 1.000 1.000 1.000 pe86m7 1.000 1.000 1.000 pe87m7 1.000 1.000 1.000 pe88m7 1.000 1.000 1.000 pe89m7 1.000 1.000 1.000 pe90m7 1.000 1.000 1.000 pe91m7 1.000 1.000 1.000 pe84m7 1.000 1.000 1.000 pe85m7 1.000 1.000 1.000 pe96m7 1.000 1.000 1.000 pe97m7 1.000 1.000 1.000 pe92m7 1.000 1.000 1.000 pe93m7 1.000 1.000 1.000 pe94m7 1.000 1.000 1.000 pe95m7 1.000 1.000 1.000 pe64m7 1.000 1.000 1.000 pe81m10 1.000 1.000 1.000 pe82m10 1.000 1.000 1.000 pe83m10 1.000 1.000 1.000 pe86m10 1.000 1.000 1.000 pe87m10 1.000 1.000 1.000 pe88m10 1.000 1.000 1.000 pe89m10 1.000 1.000 1.000 pe90m10 1.000 1.000 1.000 pe91m10 1.000 1.000 1.000 pe84m10 1.000 1.000 1.000 pe85m10 1.000 1.000 1.000 pe96m10 1.000 1.000 1.000 pe97m10 1.000 1.000 1.000 pe92m10 1.000 1.000 1.000 pe93m10 1.000 1.000 1.000 pe94m10 1.000 1.000 1.000 pe95m10 1.000 1.000 1.000 pe64m10 1.000 1.000 1.000 pe81m12 1.000 1.000 1.000 pe82m12 1.000 1.000 1.000 pe83m12 1.000 1.000 1.000 pe86m12 1.000 1.000 1.000 pe87m12 1.000 1.000 1.000 pe88m12 1.000 1.000 1.000 pe89m12 1.000 1.000 1.000 pe90m12 1.000 1.000 1.000 pe91m12 1.000 1.000 1.000 pe84m12 1.000 1.000 1.000 pe85m12 1.000 1.000 1.000 pe96m12 1.000 1.000 1.000 pe97m12 1.000 1.000 1.000 pe92m12 1.000 1.000 1.000 pe93m12 1.000 1.000 1.000 pe94m12 1.000 1.000 1.000 pe95m12 1.000 1.000 1.000 pe64m12 1.000 1.000 1.000 pe2m5 1.000 1.000 1.000 pe4m5 1.000 1.000 1.000 pe7m5 1.000 1.000 1.000 pe11m5 1.000 1.000 1.000 pe13m5 1.000 1.000 1.000 pe25m5 1.000 1.000 1.000 pe2m7 1.000 1.000 1.000 pe4m7 1.000 1.000 1.000 pe7m7 1.000 1.000 1.000 pe11m7 1.000 1.000 1.000 pe13m7 1.000 1.000 1.000 pe25m7 1.000 1.000 1.000 pe2m10 1.000 1.000 1.000 pe4m10 1.000 1.000 1.000 pe7m10 1.000 1.000 1.000 pe11m10 1.000 1.000 1.000 pe13m10 1.000 1.000 1.000 pe25m10 1.000 1.000 1.000 pe2m12 1.000 1.000 1.000 pe4m12 1.000 1.000 1.000 pe7m12 1.000 1.000 1.000 pe11m12 1.000 1.000 1.000 pe13m12 1.000 1.000 1.000 pe25m12 1.000 1.000 1.000

S4 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S3 model) Comparative Fit Index (CFI) 0. (>0.95) Change in CFI: 0.0 (decrease) - worse fit Tucker-Lewis Index (TLI) 0. (>0.95) Change in TLI: 0.0 (decrease) - worse fit RMSEA 0. (≤ 0.06) Change in RMSEA: 0.0 (increase) - worse fit 90 Percent confidence interval - lower 0. 90 Percent confidence interval - upper 0.
SRMR 0. (≤ 0.08) Change in SRMR: 0.0 (increase) - worse fit

# summary(semTools::compareFit(RICLPM_multi_adhd_S3.fit, RICLPM_multi_adhd_S4.fit, nested = TRUE)) #† indicates the best fitting model

As all fit indices changed by more than 0.01 - we cannot accept this measurement model.

Teacher report RI-CLPM: Inattention and social isolation

RICLPM_multi_inat_S1: Inattention step 1

Multiple response items RICLPM teacher report inattention ADHD symptoms and social isolation: Step 1, the configural model (S1)

RICLPMt_multi_inat_S1 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (teacher report)
  RIinat1 =~ 1*trf89e5 + 1*trf89e7 + 1*trf89e10 + 1*trf89e12
  RIinat2 =~ 1*trf90e5 + 1*trf90e7 + 1*trf90e10 + 1*trf90e12
  RIinat3 =~ 1*trf91e5 + 1*trf91e7 + 1*trf91e10 + 1*trf91e12
  RIinat4 =~ 1*trf94e5 + 1*trf94e7 + 1*trf94e10 + 1*trf94e12
  RIinat5 =~ 1*trf95e5 + 1*trf95e7 + 1*trf95e10 + 1*trf95e12
  RIinat6 =~ 1*trf96e5 + 1*trf96e7 + 1*trf96e10 + 1*trf96e12
  RIinat7 =~ 1*trf97e5 + 1*trf97e7 + 1*trf97e10 + 1*trf97e12
  RIinat8 =~ 1*trf98e5 + 1*trf98e7 + 1*trf98e10 + 1*trf98e12
  RIinat9 =~ 1*trf99e5 + 1*trf99e7 + 1*trf99e10 + 1*trf99e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves
  WFinat5 =~ trf89e5 + trf90e5 + trf91e5 + trf94e5 + trf95e5 + trf96e5 + trf97e5 + trf98e5 + trf99e5
  WFinat7 =~ trf89e7 + trf90e7 + trf91e7 + trf94e7 + trf95e7 + trf96e7 + trf97e7 + trf98e7 + trf99e7
  WFinat10 =~ trf89e10 + trf90e10 + trf91e10 + trf94e10 + trf95e10 + trf96e10 + trf97e10 + trf98e10 + trf99e10
  WFinat12 =~ trf89e12 + trf90e12 + trf91e12 + trf94e12 + trf95e12 + trf96e12 + trf97e12 + trf98e12 + trf99e12
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ trf11e5 + trf19e5 + trf24e5 + trf30e5 + trf34e5 + trf77e5 
  WFsi7 =~ trf11e7 + trf19e7 + trf24e7 + trf30e7 + trf34e7 + trf77e7 
  WFsi10 =~ trf11e10 + trf19e10 + trf24e10 + trf30e10 + trf34e10 + trf77e10 
  WFsi12 =~ trf11e12 + trf19e12 + trf24e12 + trf30e12 + trf34e12 + trf77e12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPMt_multi_inat_S1.fit <- cfa(RICLPMt_multi_inat_S1, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,        
                           missing = 'pairwise' 
)

summary(RICLPMt_multi_inat_S1.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 148 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 279

Model Test User Model: Standard Robust Test Statistic 2102.514 2100.515 Degrees of freedom 1574 1574 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.928 Shift parameter 1010.000 simple second-order correction

Model Test Baseline Model:

Test statistic 451629.807 110523.928 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.136

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.999 0.995 Tucker-Lewis Index (TLI) 0.999 0.995

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.012 0.012 90 Percent confidence interval - lower 0.011 0.011 90 Percent confidence interval - upper 0.014 0.014 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.051 0.051

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
trf89e5 1.000 0.688 0.688 trf89e7 1.000 0.688 0.688 trf89e10 1.000 0.688 0.688 trf89e12 1.000 0.688 0.688 RIinat2 =~
trf90e5 1.000 0.602 0.602 trf90e7 1.000 0.602 0.602 trf90e10 1.000 0.602 0.602 trf90e12 1.000 0.602 0.602 RIinat3 =~
trf91e5 1.000 0.657 0.657 trf91e7 1.000 0.657 0.657 trf91e10 1.000 0.657 0.657 trf91e12 1.000 0.657 0.657 RIinat4 =~
trf94e5 1.000 0.597 0.597 trf94e7 1.000 0.597 0.597 trf94e10 1.000 0.597 0.597 trf94e12 1.000 0.597 0.597 RIinat5 =~
trf95e5 1.000 0.537 0.537 trf95e7 1.000 0.537 0.537 trf95e10 1.000 0.537 0.537 trf95e12 1.000 0.537 0.537 RIinat6 =~
trf96e5 1.000 0.612 0.612 trf96e7 1.000 0.612 0.612 trf96e10 1.000 0.612 0.612 trf96e12 1.000 0.612 0.612 RIinat7 =~
trf97e5 1.000 0.590 0.590 trf97e7 1.000 0.590 0.590 trf97e10 1.000 0.590 0.590 trf97e12 1.000 0.590 0.590 RIinat8 =~
trf98e5 1.000 0.600 0.600 trf98e7 1.000 0.600 0.600 trf98e10 1.000 0.600 0.600 trf98e12 1.000 0.600 0.600 RIinat9 =~
trf99e5 1.000 0.590 0.590 trf99e7 1.000 0.590 0.590 trf99e10 1.000 0.590 0.590 trf99e12 1.000 0.590 0.590 RIsi1 =~
trf11e5 1.000 0.537 0.537 trf11e7 1.000 0.537 0.537 trf11e10 1.000 0.537 0.537 trf11e12 1.000 0.537 0.537 RIsi2 =~
trf19e5 1.000 0.600 0.600 trf19e7 1.000 0.600 0.600 trf19e10 1.000 0.600 0.600 trf19e12 1.000 0.600 0.600 RIsi3 =~
trf24e5 1.000 0.553 0.553 trf24e7 1.000 0.553 0.553 trf24e10 1.000 0.553 0.553 trf24e12 1.000 0.553 0.553 RIsi4 =~
trf30e5 1.000 0.563 0.563 trf30e7 1.000 0.563 0.563 trf30e10 1.000 0.563 0.563 trf30e12 1.000 0.563 0.563 RIsi5 =~
trf34e5 1.000 0.604 0.604 trf34e7 1.000 0.604 0.604 trf34e10 1.000 0.604 0.604 trf34e12 1.000 0.604 0.604 RIsi6 =~
trf77e5 1.000 0.565 0.565 trf77e7 1.000 0.565 0.565 trf77e10 1.000 0.565 0.565 trf77e12 1.000 0.565 0.565 WFinat5 =~
trf89e5 1.000 0.651 0.651 trf90e5 1.045 0.026 39.597 0.000 0.681 0.681 trf91e5 1.031 0.024 42.169 0.000 0.671 0.671 trf94e5 1.023 0.029 34.703 0.000 0.666 0.666 trf95e5 0.921 0.047 19.498 0.000 0.599 0.599 trf96e5 0.947 0.034 28.129 0.000 0.616 0.616 trf97e5 1.001 0.030 33.097 0.000 0.652 0.652 trf98e5 0.801 0.050 15.951 0.000 0.522 0.522 trf99e5 0.991 0.038 25.926 0.000 0.645 0.645 WFinat7 =~
trf89e7 1.000 0.632 0.632 trf90e7 1.060 0.029 37.090 0.000 0.670 0.670 trf91e7 1.085 0.026 41.495 0.000 0.686 0.686 trf94e7 1.018 0.032 31.849 0.000 0.644 0.644 trf95e7 1.009 0.049 20.667 0.000 0.638 0.638 trf96e7 1.048 0.033 31.938 0.000 0.663 0.663 trf97e7 1.095 0.031 35.344 0.000 0.692 0.692 trf98e7 0.802 0.057 14.121 0.000 0.507 0.507 trf99e7 1.000 0.039 25.320 0.000 0.632 0.632 WFinat10 =~
trf89e10 1.000 0.618 0.618 trf90e10 1.094 0.031 35.311 0.000 0.676 0.676 trf91e10 1.069 0.031 34.729 0.000 0.660 0.660 trf94e10 1.074 0.037 29.320 0.000 0.664 0.664 trf95e10 1.072 0.053 20.325 0.000 0.662 0.662 trf96e10 1.066 0.037 28.941 0.000 0.658 0.658 trf97e10 1.141 0.038 30.264 0.000 0.705 0.705 trf98e10 0.975 0.054 18.071 0.000 0.602 0.602 trf99e10 1.095 0.046 23.735 0.000 0.676 0.676 WFinat12 =~
trf89e12 1.000 0.645 0.645 trf90e12 1.058 0.030 35.085 0.000 0.683 0.683 trf91e12 1.013 0.028 35.896 0.000 0.654 0.654 trf94e12 1.118 0.032 34.743 0.000 0.721 0.721 trf95e12 1.014 0.048 21.012 0.000 0.654 0.654 trf96e12 1.028 0.035 29.438 0.000 0.663 0.663 trf97e12 1.102 0.032 34.033 0.000 0.711 0.711 trf98e12 1.030 0.050 20.511 0.000 0.665 0.665 trf99e12 1.062 0.042 25.549 0.000 0.685 0.685 WFsi5 =~
trf11e5 1.000 0.416 0.416 trf19e5 1.571 0.220 7.146 0.000 0.653 0.653 trf24e5 1.385 0.184 7.540 0.000 0.576 0.576 trf30e5 1.188 0.167 7.115 0.000 0.494 0.494 trf34e5 1.443 0.212 6.822 0.000 0.600 0.600 trf77e5 1.664 0.218 7.626 0.000 0.692 0.692 WFsi7 =~
trf11e7 1.000 0.595 0.595 trf19e7 0.941 0.086 10.904 0.000 0.560 0.560 trf24e7 1.132 0.115 9.846 0.000 0.673 0.673 trf30e7 0.856 0.100 8.574 0.000 0.509 0.509 trf34e7 1.085 0.108 10.035 0.000 0.645 0.645 trf77e7 1.135 0.108 10.550 0.000 0.675 0.675 WFsi10 =~
trf11e10 1.000 0.642 0.642 trf19e10 1.047 0.094 11.081 0.000 0.672 0.672 trf24e10 0.996 0.104 9.586 0.000 0.639 0.639 trf30e10 0.856 0.103 8.336 0.000 0.550 0.550 trf34e10 1.010 0.095 10.634 0.000 0.648 0.648 trf77e10 1.062 0.105 10.133 0.000 0.682 0.682 WFsi12 =~
trf11e12 1.000 0.657 0.657 trf19e12 1.004 0.100 10.039 0.000 0.659 0.659 trf24e12 0.912 0.107 8.522 0.000 0.598 0.598 trf30e12 0.914 0.102 8.976 0.000 0.600 0.600 trf34e12 1.032 0.099 10.413 0.000 0.678 0.678 trf77e12 0.978 0.096 10.154 0.000 0.642 0.642

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.361 0.056 6.451 0.000 0.372 0.372 WFsi5 -0.350 0.118 -2.957 0.003 -0.230 -0.230 WFsi7 ~
WFinat5 -0.096 0.060 -1.608 0.108 -0.105 -0.105 WFsi5 0.403 0.130 3.097 0.002 0.282 0.282 WFinat10 ~
WFinat7 0.355 0.065 5.456 0.000 0.363 0.363 WFsi7 -0.275 0.080 -3.441 0.001 -0.265 -0.265 WFsi10 ~
WFinat7 0.111 0.083 1.342 0.180 0.110 0.110 WFsi7 0.075 0.108 0.691 0.490 0.069 0.069 WFinat12 ~
WFinat10 0.275 0.070 3.944 0.000 0.263 0.263 WFsi10 -0.093 0.078 -1.193 0.233 -0.092 -0.092 WFsi12 ~
WFinat10 -0.038 0.078 -0.488 0.626 -0.036 -0.036 WFsi10 0.314 0.086 3.652 0.000 0.307 0.307

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.103 0.020 5.015 0.000 0.379 0.379 .WFinat7 ~~
.WFsi7 0.162 0.023 7.024 0.000 0.477 0.477 .WFinat10 ~~
.WFsi10 0.171 0.024 7.121 0.000 0.468 0.468 .WFinat12 ~~
.WFsi12 0.194 0.026 7.497 0.000 0.492 0.492 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.429 0.026 16.587 0.000 1.035 1.035 RIinat3 0.471 0.026 18.283 0.000 1.042 1.042 RIinat4 0.373 0.025 14.727 0.000 0.908 0.908 RIinat5 0.313 0.026 12.024 0.000 0.845 0.845 RIinat6 0.372 0.026 14.444 0.000 0.882 0.882 RIinat7 0.385 0.027 14.370 0.000 0.949 0.949 RIinat8 0.296 0.025 11.607 0.000 0.716 0.716 RIinat9 0.359 0.027 13.423 0.000 0.883 0.883 RIsi1 0.161 0.026 6.215 0.000 0.435 0.435 RIsi2 0.325 0.022 14.674 0.000 0.787 0.787 RIsi3 0.181 0.028 6.529 0.000 0.474 0.474 RIsi4 0.168 0.023 7.174 0.000 0.434 0.434 RIsi5 0.325 0.024 13.336 0.000 0.781 0.781 RIsi6 0.188 0.023 8.072 0.000 0.484 0.484 RIinat2 ~~
RIinat3 0.391 0.026 14.977 0.000 0.990 0.990 RIinat4 0.326 0.025 12.806 0.000 0.907 0.907 RIinat5 0.266 0.026 10.133 0.000 0.822 0.822 RIinat6 0.336 0.026 12.910 0.000 0.911 0.911 RIinat7 0.342 0.027 12.838 0.000 0.963 0.963 RIinat8 0.264 0.026 10.224 0.000 0.732 0.732 RIinat9 0.315 0.027 11.575 0.000 0.887 0.887 RIsi1 0.152 0.025 6.136 0.000 0.470 0.470 RIsi2 0.290 0.021 13.537 0.000 0.803 0.803 RIsi3 0.177 0.027 6.680 0.000 0.532 0.532 RIsi4 0.161 0.023 7.159 0.000 0.476 0.476 RIsi5 0.286 0.023 12.301 0.000 0.786 0.786 RIsi6 0.178 0.023 7.634 0.000 0.523 0.523 RIinat3 ~~
RIinat4 0.378 0.026 14.669 0.000 0.965 0.965 RIinat5 0.301 0.026 11.520 0.000 0.853 0.853 RIinat6 0.354 0.026 13.573 0.000 0.880 0.880 RIinat7 0.365 0.027 13.415 0.000 0.943 0.943 RIinat8 0.276 0.027 10.424 0.000 0.701 0.701 RIinat9 0.315 0.027 11.487 0.000 0.813 0.813 RIsi1 0.141 0.025 5.713 0.000 0.400 0.400 RIsi2 0.335 0.022 15.567 0.000 0.850 0.850 RIsi3 0.179 0.026 6.879 0.000 0.493 0.493 RIsi4 0.121 0.023 5.245 0.000 0.327 0.327 RIsi5 0.326 0.024 13.813 0.000 0.822 0.822 RIsi6 0.135 0.023 5.927 0.000 0.363 0.363 RIinat4 ~~
RIinat5 0.277 0.025 10.880 0.000 0.864 0.864 RIinat6 0.338 0.025 13.405 0.000 0.926 0.926 RIinat7 0.349 0.026 13.295 0.000 0.990 0.990 RIinat8 0.277 0.025 11.032 0.000 0.773 0.773 RIinat9 0.291 0.026 11.011 0.000 0.827 0.827 RIsi1 0.154 0.024 6.332 0.000 0.479 0.479 RIsi2 0.330 0.021 15.493 0.000 0.921 0.921 RIsi3 0.194 0.026 7.328 0.000 0.588 0.588 RIsi4 0.108 0.023 4.723 0.000 0.322 0.322 RIsi5 0.326 0.023 14.110 0.000 0.905 0.905 RIsi6 0.105 0.024 4.456 0.000 0.312 0.312 RIinat5 ~~
RIinat6 0.300 0.026 11.519 0.000 0.913 0.913 RIinat7 0.256 0.027 9.562 0.000 0.808 0.808 RIinat8 0.268 0.026 10.290 0.000 0.832 0.832 RIinat9 0.296 0.028 10.592 0.000 0.934 0.934 RIsi1 0.131 0.029 4.545 0.000 0.452 0.452 RIsi2 0.316 0.024 13.253 0.000 0.979 0.979 RIsi3 0.192 0.030 6.419 0.000 0.647 0.647 RIsi4 0.211 0.026 8.265 0.000 0.699 0.699 RIsi5 0.309 0.026 11.950 0.000 0.951 0.951 RIsi6 0.228 0.025 9.038 0.000 0.749 0.749 RIinat6 ~~
RIinat7 0.354 0.026 13.469 0.000 0.980 0.980 RIinat8 0.380 0.025 15.363 0.000 1.034 1.034 RIinat9 0.340 0.027 12.680 0.000 0.942 0.942 RIsi1 0.161 0.025 6.334 0.000 0.489 0.489 RIsi2 0.311 0.021 14.564 0.000 0.847 0.847 RIsi3 0.206 0.026 7.903 0.000 0.607 0.607 RIsi4 0.175 0.023 7.575 0.000 0.508 0.508 RIsi5 0.297 0.024 12.603 0.000 0.802 0.802 RIsi6 0.165 0.023 7.051 0.000 0.476 0.476 RIinat7 ~~
RIinat8 0.278 0.027 10.476 0.000 0.784 0.784 RIinat9 0.306 0.028 11.116 0.000 0.880 0.880 RIsi1 0.162 0.025 6.492 0.000 0.511 0.511 RIsi2 0.327 0.022 14.889 0.000 0.924 0.924 RIsi3 0.201 0.026 7.586 0.000 0.616 0.616 RIsi4 0.159 0.023 6.939 0.000 0.479 0.479 RIsi5 0.308 0.024 12.855 0.000 0.862 0.862 RIsi6 0.168 0.023 7.249 0.000 0.503 0.503 RIinat8 ~~
RIinat9 0.341 0.026 12.917 0.000 0.963 0.963 RIsi1 0.209 0.025 8.405 0.000 0.650 0.650 RIsi2 0.275 0.023 11.933 0.000 0.764 0.764 RIsi3 0.256 0.027 9.432 0.000 0.769 0.769 RIsi4 0.181 0.024 7.516 0.000 0.535 0.535 RIsi5 0.276 0.026 10.500 0.000 0.761 0.761 RIsi6 0.144 0.024 6.051 0.000 0.424 0.424 RIinat9 ~~
RIsi1 0.176 0.027 6.516 0.000 0.554 0.554 RIsi2 0.247 0.023 10.532 0.000 0.697 0.697 RIsi3 0.197 0.028 6.915 0.000 0.603 0.603 RIsi4 0.179 0.023 7.690 0.000 0.541 0.541 RIsi5 0.245 0.027 9.129 0.000 0.688 0.688 RIsi6 0.205 0.023 8.864 0.000 0.614 0.614 RIsi1 ~~
RIsi2 0.195 0.030 6.433 0.000 0.604 0.604 RIsi3 0.364 0.037 9.809 0.000 1.226 1.226 RIsi4 0.179 0.030 5.984 0.000 0.591 0.591 RIsi5 0.197 0.035 5.616 0.000 0.605 0.605 RIsi6 0.131 0.032 4.052 0.000 0.431 0.431 RIsi2 ~~
RIsi3 0.232 0.032 7.279 0.000 0.699 0.699 RIsi4 0.166 0.029 5.708 0.000 0.490 0.490 RIsi5 0.404 0.031 12.862 0.000 1.113 1.113 RIsi6 0.101 0.032 3.185 0.001 0.297 0.297 RIsi3 ~~
RIsi4 0.113 0.032 3.580 0.000 0.362 0.362 RIsi5 0.221 0.037 5.907 0.000 0.659 0.659 RIsi6 0.048 0.033 1.443 0.149 0.152 0.152 RIsi4 ~~
RIsi5 0.173 0.032 5.496 0.000 0.510 0.510 RIsi6 0.333 0.032 10.559 0.000 1.047 1.047 RIsi5 ~~
RIsi6 0.110 0.035 3.127 0.002 0.321 0.321

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf89e5 0.000 0.000 0.000 .trf89e7 0.000 0.000 0.000 .trf89e10 0.000 0.000 0.000 .trf89e12 0.000 0.000 0.000 .trf90e5 0.000 0.000 0.000 .trf90e7 0.000 0.000 0.000 .trf90e10 0.000 0.000 0.000 .trf90e12 0.000 0.000 0.000 .trf91e5 0.000 0.000 0.000 .trf91e7 0.000 0.000 0.000 .trf91e10 0.000 0.000 0.000 .trf91e12 0.000 0.000 0.000 .trf94e5 0.000 0.000 0.000 .trf94e7 0.000 0.000 0.000 .trf94e10 0.000 0.000 0.000 .trf94e12 0.000 0.000 0.000 .trf95e5 0.000 0.000 0.000 .trf95e7 0.000 0.000 0.000 .trf95e10 0.000 0.000 0.000 .trf95e12 0.000 0.000 0.000 .trf96e5 0.000 0.000 0.000 .trf96e7 0.000 0.000 0.000 .trf96e10 0.000 0.000 0.000 .trf96e12 0.000 0.000 0.000 .trf97e5 0.000 0.000 0.000 .trf97e7 0.000 0.000 0.000 .trf97e10 0.000 0.000 0.000 .trf97e12 0.000 0.000 0.000 .trf98e5 0.000 0.000 0.000 .trf98e7 0.000 0.000 0.000 .trf98e10 0.000 0.000 0.000 .trf98e12 0.000 0.000 0.000 .trf99e5 0.000 0.000 0.000 .trf99e7 0.000 0.000 0.000 .trf99e10 0.000 0.000 0.000 .trf99e12 0.000 0.000 0.000 .trf11e5 0.000 0.000 0.000 .trf11e7 0.000 0.000 0.000 .trf11e10 0.000 0.000 0.000 .trf11e12 0.000 0.000 0.000 .trf19e5 0.000 0.000 0.000 .trf19e7 0.000 0.000 0.000 .trf19e10 0.000 0.000 0.000 .trf19e12 0.000 0.000 0.000 .trf24e5 0.000 0.000 0.000 .trf24e7 0.000 0.000 0.000 .trf24e10 0.000 0.000 0.000 .trf24e12 0.000 0.000 0.000 .trf30e5 0.000 0.000 0.000 .trf30e7 0.000 0.000 0.000 .trf30e10 0.000 0.000 0.000 .trf30e12 0.000 0.000 0.000 .trf34e5 0.000 0.000 0.000 .trf34e7 0.000 0.000 0.000 .trf34e10 0.000 0.000 0.000 .trf34e12 0.000 0.000 0.000 .trf77e5 0.000 0.000 0.000 .trf77e7 0.000 0.000 0.000 .trf77e10 0.000 0.000 0.000 .trf77e12 0.000 0.000 0.000 RIinat1 0.000 0.000 0.000 RIinat2 0.000 0.000 0.000 RIinat3 0.000 0.000 0.000 RIinat4 0.000 0.000 0.000 RIinat5 0.000 0.000 0.000 RIinat6 0.000 0.000 0.000 RIinat7 0.000 0.000 0.000 RIinat8 0.000 0.000 0.000 RIinat9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 .WFinat7 0.000 0.000 0.000 .WFinat10 0.000 0.000 0.000 .WFinat12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf89e5|t1 0.463 0.029 16.194 0.000 0.463 0.463 trf89e5|t2 1.304 0.038 34.409 0.000 1.304 1.304 trf89e7|t1 0.606 0.030 20.286 0.000 0.606 0.606 trf89e7|t2 1.451 0.042 34.775 0.000 1.451 1.451 trf89e10|t1 0.720 0.032 22.840 0.000 0.720 0.720 trf89e10|t2 1.595 0.047 34.129 0.000 1.595 1.595 trf89e12|t1 0.772 0.033 23.143 0.000 0.772 0.772 trf89e12|t2 1.708 0.053 32.459 0.000 1.708 1.708 trf90e5|t1 0.544 0.029 18.769 0.000 0.544 0.544 trf90e5|t2 1.580 0.044 35.600 0.000 1.580 1.580 trf90e7|t1 0.584 0.030 19.646 0.000 0.584 0.584 trf90e7|t2 1.538 0.044 34.976 0.000 1.538 1.538 trf90e10|t1 0.671 0.031 21.558 0.000 0.671 0.671 trf90e10|t2 1.742 0.052 33.723 0.000 1.742 1.742 trf90e12|t1 0.732 0.033 22.121 0.000 0.732 0.732 trf90e12|t2 1.860 0.059 31.525 0.000 1.860 1.860 trf91e5|t1 0.241 0.028 8.704 0.000 0.241 0.241 trf91e5|t2 1.294 0.038 34.351 0.000 1.294 1.294 trf91e7|t1 0.307 0.028 10.800 0.000 0.307 0.307 trf91e7|t2 1.355 0.040 34.272 0.000 1.355 1.355 trf91e10|t1 0.386 0.029 13.122 0.000 0.386 0.386 trf91e10|t2 1.484 0.044 34.010 0.000 1.484 1.484 trf91e12|t1 0.448 0.031 14.487 0.000 0.448 0.448 trf91e12|t2 1.583 0.048 32.761 0.000 1.583 1.583 trf94e5|t1 0.385 0.028 13.608 0.000 0.385 0.385 trf94e5|t2 1.513 0.043 35.428 0.000 1.513 1.513 trf94e7|t1 0.401 0.029 13.944 0.000 0.401 0.401 trf94e7|t2 1.514 0.043 34.958 0.000 1.514 1.514 trf94e10|t1 0.408 0.030 13.784 0.000 0.408 0.408 trf94e10|t2 1.579 0.046 34.063 0.000 1.579 1.579 trf94e12|t1 0.526 0.032 16.688 0.000 0.526 0.526 trf94e12|t2 1.718 0.053 32.338 0.000 1.718 1.718 trf95e5|t1 0.966 0.033 29.590 0.000 0.966 0.966 trf95e5|t2 1.953 0.058 33.569 0.000 1.953 1.953 trf95e7|t1 1.111 0.035 31.477 0.000 1.111 1.111 trf95e7|t2 2.145 0.070 30.601 0.000 2.145 2.145 trf95e10|t1 1.177 0.037 31.683 0.000 1.177 1.177 trf95e10|t2 2.210 0.076 29.033 0.000 2.210 2.210 trf95e12|t1 1.072 0.037 28.956 0.000 1.072 1.072 trf95e12|t2 2.298 0.086 26.577 0.000 2.298 2.298 trf96e5|t1 0.567 0.029 19.249 0.000 0.567 0.567 trf96e5|t2 1.659 0.047 35.107 0.000 1.659 1.659 trf96e7|t1 0.474 0.029 16.284 0.000 0.474 0.474 trf96e7|t2 1.629 0.047 34.953 0.000 1.629 1.629 trf96e10|t1 0.635 0.031 20.583 0.000 0.635 0.635 trf96e10|t2 1.594 0.047 34.094 0.000 1.594 1.594 trf96e12|t1 0.633 0.032 19.653 0.000 0.633 0.633 trf96e12|t2 1.617 0.050 32.663 0.000 1.617 1.617 trf97e5|t1 0.435 0.029 15.260 0.000 0.435 0.435 trf97e5|t2 1.556 0.044 35.483 0.000 1.556 1.556 trf97e7|t1 0.525 0.029 17.887 0.000 0.525 0.525 trf97e7|t2 1.547 0.044 35.020 0.000 1.547 1.547 trf97e10|t1 0.750 0.032 23.618 0.000 0.750 0.750 trf97e10|t2 1.649 0.048 34.055 0.000 1.649 1.649 trf97e12|t1 0.838 0.034 24.474 0.000 0.838 0.838 trf97e12|t2 1.709 0.053 32.255 0.000 1.709 1.709 trf98e5|t1 0.975 0.033 29.611 0.000 0.975 0.975 trf98e5|t2 1.948 0.058 33.443 0.000 1.948 1.948 trf98e7|t1 1.048 0.034 30.427 0.000 1.048 1.048 trf98e7|t2 2.004 0.062 32.305 0.000 2.004 2.004 trf98e10|t1 1.196 0.038 31.780 0.000 1.196 1.196 trf98e10|t2 1.905 0.059 32.492 0.000 1.905 1.905 trf98e12|t1 1.339 0.043 30.975 0.000 1.339 1.339 trf98e12|t2 2.056 0.071 28.905 0.000 2.056 2.056 trf99e5|t1 0.843 0.031 26.870 0.000 0.843 0.843 trf99e5|t2 1.943 0.058 33.608 0.000 1.943 1.943 trf99e7|t1 0.790 0.031 25.181 0.000 0.790 0.790 trf99e7|t2 1.862 0.055 33.791 0.000 1.862 1.862 trf99e10|t1 1.075 0.036 30.197 0.000 1.075 1.075 trf99e10|t2 2.025 0.065 31.380 0.000 2.025 2.025 trf99e12|t1 1.133 0.038 29.572 0.000 1.133 1.133 trf99e12|t2 2.050 0.069 29.607 0.000 2.050 2.050 trf11e5|t1 1.531 0.043 35.552 0.000 1.531 1.531 trf11e5|t2 2.557 0.105 24.434 0.000 2.557 2.557 trf11e7|t1 1.350 0.039 34.280 0.000 1.350 1.350 trf11e7|t2 2.350 0.085 27.639 0.000 2.350 2.350 trf11e10|t1 1.425 0.042 33.839 0.000 1.425 1.425 trf11e10|t2 2.181 0.074 29.469 0.000 2.181 2.181 trf11e12|t1 1.521 0.047 32.702 0.000 1.521 1.521 trf11e12|t2 2.532 0.111 22.893 0.000 2.532 2.532 trf19e5|t1 1.141 0.035 32.581 0.000 1.141 1.141 trf19e5|t2 2.187 0.071 30.640 0.000 2.187 2.187 trf19e7|t1 0.970 0.033 29.223 0.000 0.970 0.970 trf19e7|t2 2.231 0.076 29.522 0.000 2.231 2.231 trf19e10|t1 0.873 0.033 26.515 0.000 0.873 0.873 trf19e10|t2 2.017 0.064 31.562 0.000 2.017 2.017 trf19e12|t1 0.892 0.034 25.877 0.000 0.892 0.892 trf19e12|t2 2.084 0.070 29.579 0.000 2.084 2.084 trf24e5|t1 1.745 0.050 35.075 0.000 1.745 1.745 trf24e5|t2 2.624 0.113 23.162 0.000 2.624 2.624 trf24e7|t1 1.559 0.045 35.002 0.000 1.559 1.559 trf24e7|t2 2.460 0.096 25.730 0.000 2.460 2.460 trf24e10|t1 1.580 0.046 34.099 0.000 1.580 1.580 trf24e10|t2 2.468 0.099 24.933 0.000 2.468 2.468 trf24e12|t1 1.648 0.051 32.380 0.000 1.648 1.648 trf24e12|t2 2.649 0.128 20.769 0.000 2.649 2.649 trf30e5|t1 1.120 0.035 32.299 0.000 1.120 1.120 trf30e5|t2 2.104 0.066 31.833 0.000 2.104 2.104 trf30e7|t1 1.260 0.038 33.515 0.000 1.260 1.260 trf30e7|t2 2.295 0.080 28.532 0.000 2.295 2.295 trf30e10|t1 1.306 0.039 33.065 0.000 1.306 1.306 trf30e10|t2 2.293 0.082 27.830 0.000 2.293 2.293 trf30e12|t1 1.098 0.038 29.149 0.000 1.098 1.098 trf30e12|t2 2.128 0.074 28.736 0.000 2.128 2.128 trf34e5|t1 1.721 0.049 35.327 0.000 1.721 1.721 trf34e5|t2 2.627 0.113 23.206 0.000 2.627 2.627 trf34e7|t1 1.640 0.047 34.994 0.000 1.640 1.640 trf34e7|t2 2.971 0.177 16.764 0.000 2.971 2.971 trf34e10|t1 1.268 0.039 32.750 0.000 1.268 1.268 trf34e10|t2 2.312 0.084 27.556 0.000 2.312 2.312 trf34e12|t1 1.267 0.040 31.336 0.000 1.267 1.267 trf34e12|t2 2.467 0.103 23.948 0.000 2.467 2.467 trf77e5|t1 1.058 0.034 31.255 0.000 1.058 1.058 trf77e5|t2 2.060 0.064 32.328 0.000 2.060 2.060 trf77e7|t1 1.183 0.036 32.585 0.000 1.183 1.183 trf77e7|t2 2.230 0.076 29.496 0.000 2.230 2.230 trf77e10|t1 1.208 0.038 32.018 0.000 1.208 1.208 trf77e10|t2 2.194 0.075 29.225 0.000 2.194 2.194 trf77e12|t1 1.040 0.037 28.350 0.000 1.040 1.040 trf77e12|t2 1.977 0.065 30.511 0.000 1.977 1.977

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf89e5 0.102 0.102 0.102 .trf89e7 0.126 0.126 0.126 .trf89e10 0.144 0.144 0.144 .trf89e12 0.110 0.110 0.110 .trf90e5 0.175 0.175 0.175 .trf90e7 0.189 0.189 0.189 .trf90e10 0.180 0.180 0.180 .trf90e12 0.171 0.171 0.171 .trf91e5 0.118 0.118 0.118 .trf91e7 0.098 0.098 0.098 .trf91e10 0.132 0.132 0.132 .trf91e12 0.141 0.141 0.141 .trf94e5 0.200 0.200 0.200 .trf94e7 0.229 0.229 0.229 .trf94e10 0.204 0.204 0.204 .trf94e12 0.124 0.124 0.124 .trf95e5 0.352 0.352 0.352 .trf95e7 0.304 0.304 0.304 .trf95e10 0.273 0.273 0.273 .trf95e12 0.283 0.283 0.283 .trf96e5 0.245 0.245 0.245 .trf96e7 0.186 0.186 0.186 .trf96e10 0.192 0.192 0.192 .trf96e12 0.185 0.185 0.185 .trf97e5 0.227 0.227 0.227 .trf97e7 0.172 0.172 0.172 .trf97e10 0.155 0.155 0.155 .trf97e12 0.146 0.146 0.146 .trf98e5 0.368 0.368 0.368 .trf98e7 0.383 0.383 0.383 .trf98e10 0.277 0.277 0.277 .trf98e12 0.198 0.198 0.198 .trf99e5 0.236 0.236 0.236 .trf99e7 0.253 0.253 0.253 .trf99e10 0.195 0.195 0.195 .trf99e12 0.183 0.183 0.183 .trf11e5 0.538 0.538 0.538 .trf11e7 0.358 0.358 0.358 .trf11e10 0.299 0.299 0.299 .trf11e12 0.280 0.280 0.280 .trf19e5 0.213 0.213 0.213 .trf19e7 0.326 0.326 0.326 .trf19e10 0.188 0.188 0.188 .trf19e12 0.206 0.206 0.206 .trf24e5 0.362 0.362 0.362 .trf24e7 0.240 0.240 0.240 .trf24e10 0.285 0.285 0.285 .trf24e12 0.336 0.336 0.336 .trf30e5 0.439 0.439 0.439 .trf30e7 0.424 0.424 0.424 .trf30e10 0.381 0.381 0.381 .trf30e12 0.324 0.324 0.324 .trf34e5 0.274 0.274 0.274 .trf34e7 0.218 0.218 0.218 .trf34e10 0.214 0.214 0.214 .trf34e12 0.176 0.176 0.176 .trf77e5 0.201 0.201 0.201 .trf77e7 0.224 0.224 0.224 .trf77e10 0.216 0.216 0.216 .trf77e12 0.268 0.268 0.268 RIinat1 0.474 0.027 17.705 0.000 1.000 1.000 RIinat2 0.362 0.028 12.926 0.000 1.000 1.000 RIinat3 0.432 0.027 15.747 0.000 1.000 1.000 RIinat4 0.356 0.027 13.340 0.000 1.000 1.000 RIinat5 0.289 0.031 9.228 0.000 1.000 1.000 RIinat6 0.375 0.027 13.693 0.000 1.000 1.000 RIinat7 0.348 0.030 11.713 0.000 1.000 1.000 RIinat8 0.360 0.030 12.140 0.000 1.000 1.000 RIinat9 0.348 0.032 11.002 0.000 1.000 1.000 RIsi1 0.289 0.042 6.826 0.000 1.000 1.000 RIsi2 0.360 0.031 11.735 0.000 1.000 1.000 RIsi3 0.306 0.045 6.826 0.000 1.000 1.000 RIsi4 0.316 0.034 9.326 0.000 1.000 1.000 RIsi5 0.365 0.037 9.836 0.000 1.000 1.000 RIsi6 0.320 0.035 9.039 0.000 1.000 1.000 WFinat5 0.424 0.027 15.539 0.000 1.000 1.000 .WFinat7 0.349 0.022 15.602 0.000 0.873 0.873 .WFinat10 0.332 0.023 14.320 0.000 0.870 0.870 .WFinat12 0.393 0.023 16.885 0.000 0.944 0.944 WFsi5 0.173 0.046 3.741 0.000 1.000 1.000 .WFsi7 0.330 0.049 6.726 0.000 0.932 0.932 .WFsi10 0.403 0.063 6.422 0.000 0.977 0.977 .WFsi12 0.394 0.064 6.197 0.000 0.914 0.914

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf89e5 1.000 1.000 1.000 trf89e7 1.000 1.000 1.000 trf89e10 1.000 1.000 1.000 trf89e12 1.000 1.000 1.000 trf90e5 1.000 1.000 1.000 trf90e7 1.000 1.000 1.000 trf90e10 1.000 1.000 1.000 trf90e12 1.000 1.000 1.000 trf91e5 1.000 1.000 1.000 trf91e7 1.000 1.000 1.000 trf91e10 1.000 1.000 1.000 trf91e12 1.000 1.000 1.000 trf94e5 1.000 1.000 1.000 trf94e7 1.000 1.000 1.000 trf94e10 1.000 1.000 1.000 trf94e12 1.000 1.000 1.000 trf95e5 1.000 1.000 1.000 trf95e7 1.000 1.000 1.000 trf95e10 1.000 1.000 1.000 trf95e12 1.000 1.000 1.000 trf96e5 1.000 1.000 1.000 trf96e7 1.000 1.000 1.000 trf96e10 1.000 1.000 1.000 trf96e12 1.000 1.000 1.000 trf97e5 1.000 1.000 1.000 trf97e7 1.000 1.000 1.000 trf97e10 1.000 1.000 1.000 trf97e12 1.000 1.000 1.000 trf98e5 1.000 1.000 1.000 trf98e7 1.000 1.000 1.000 trf98e10 1.000 1.000 1.000 trf98e12 1.000 1.000 1.000 trf99e5 1.000 1.000 1.000 trf99e7 1.000 1.000 1.000 trf99e10 1.000 1.000 1.000 trf99e12 1.000 1.000 1.000 trf11e5 1.000 1.000 1.000 trf11e7 1.000 1.000 1.000 trf11e10 1.000 1.000 1.000 trf11e12 1.000 1.000 1.000 trf19e5 1.000 1.000 1.000 trf19e7 1.000 1.000 1.000 trf19e10 1.000 1.000 1.000 trf19e12 1.000 1.000 1.000 trf24e5 1.000 1.000 1.000 trf24e7 1.000 1.000 1.000 trf24e10 1.000 1.000 1.000 trf24e12 1.000 1.000 1.000 trf30e5 1.000 1.000 1.000 trf30e7 1.000 1.000 1.000 trf30e10 1.000 1.000 1.000 trf30e12 1.000 1.000 1.000 trf34e5 1.000 1.000 1.000 trf34e7 1.000 1.000 1.000 trf34e10 1.000 1.000 1.000 trf34e12 1.000 1.000 1.000 trf77e5 1.000 1.000 1.000 trf77e7 1.000 1.000 1.000 trf77e10 1.000 1.000 1.000 trf77e12 1.000 1.000 1.000

For the pairwise deletion model without robust standard errors: S1 Model fit (robust indices): Comparative Fit Index (CFI) 0.995 (>0.95) Tucker-Lewis Index (TLI) 0.995 (>0.95)
RMSEA 0.012 (≤ 0.06)
90 Percent confidence interval - lower 0.011 90 Percent confidence interval - upper 0.014
SRMR 0.051 (≤ 0.08)

We can conclude that the model shows very good fit.

RICLPMt_multi_inat_S2: Inattention step 2

Multiple response items RICLPMt teacher report inattention ADHD symptoms and social isolation: Step 2.

In our second step model, we constrain the factor loadings to be invariant over time using the labels a*, b*, c*, d* etc, in the “within” part of the model.

RICLPMt_multi_inat_S2 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (teacher report)
  RIinat1 =~ 1*trf89e5 + 1*trf89e7 + 1*trf89e10 + 1*trf89e12
  RIinat2 =~ 1*trf90e5 + 1*trf90e7 + 1*trf90e10 + 1*trf90e12
  RIinat3 =~ 1*trf91e5 + 1*trf91e7 + 1*trf91e10 + 1*trf91e12
  RIinat4 =~ 1*trf94e5 + 1*trf94e7 + 1*trf94e10 + 1*trf94e12
  RIinat5 =~ 1*trf95e5 + 1*trf95e7 + 1*trf95e10 + 1*trf95e12
  RIinat6 =~ 1*trf96e5 + 1*trf96e7 + 1*trf96e10 + 1*trf96e12
  RIinat7 =~ 1*trf97e5 + 1*trf97e7 + 1*trf97e10 + 1*trf97e12
  RIinat8 =~ 1*trf98e5 + 1*trf98e7 + 1*trf98e10 + 1*trf98e12
  RIinat9 =~ 1*trf99e5 + 1*trf99e7 + 1*trf99e10 + 1*trf99e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  WFinat5 =~ a*trf89e5 + b*trf90e5 + c*trf91e5 + d*trf94e5 + e*trf95e5 + f*trf96e5 + g*trf97e5 + h*trf98e5 + i*trf99e5
  WFinat7 =~ a*trf89e7 + b*trf90e7 + c*trf91e7 + d*trf94e7 + e*trf95e7 + f*trf96e7 + g*trf97e7 + h*trf98e7 + i*trf99e7
  WFinat10 =~ a*trf89e10 + b*trf90e10 + c*trf91e10 + d*trf94e10 + e*trf95e10 + f*trf96e10 + g*trf97e10 + h*trf98e10 + i*trf99e10
  WFinat12 =~ a*trf89e12 + b*trf90e12 + c*trf91e12 + d*trf94e12 + e*trf95e12 + f*trf96e12 + g*trf97e12 + h*trf98e12 + i*trf99e12
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  WFsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  WFsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  WFsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPMt_multi_inat_S2.fit <- cfa(RICLPMt_multi_inat_S2, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPMt_multi_inat_S2.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 118 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316 Number of equality constraints 39

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 279

Model Test User Model: Standard Robust Test Statistic 2344.331 2188.435 Degrees of freedom 1613 1613 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.034 Shift parameter 1036.037 simple second-order correction

Model Test Baseline Model:

Test statistic 451629.807 110523.928 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.136

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.998 0.995 Tucker-Lewis Index (TLI) 0.998 0.994

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.014 0.013 90 Percent confidence interval - lower 0.013 0.011 90 Percent confidence interval - upper 0.016 0.014 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.052 0.052

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
trf89e5 1.000 0.688 0.688 trf89e7 1.000 0.688 0.688 trf89e10 1.000 0.688 0.688 trf89e12 1.000 0.688 0.688 RIinat2 =~
trf90e5 1.000 0.601 0.601 trf90e7 1.000 0.601 0.601 trf90e10 1.000 0.601 0.601 trf90e12 1.000 0.601 0.601 RIinat3 =~
trf91e5 1.000 0.657 0.657 trf91e7 1.000 0.657 0.657 trf91e10 1.000 0.657 0.657 trf91e12 1.000 0.657 0.657 RIinat4 =~
trf94e5 1.000 0.593 0.593 trf94e7 1.000 0.593 0.593 trf94e10 1.000 0.593 0.593 trf94e12 1.000 0.593 0.593 RIinat5 =~
trf95e5 1.000 0.536 0.536 trf95e7 1.000 0.536 0.536 trf95e10 1.000 0.536 0.536 trf95e12 1.000 0.536 0.536 RIinat6 =~
trf96e5 1.000 0.611 0.611 trf96e7 1.000 0.611 0.611 trf96e10 1.000 0.611 0.611 trf96e12 1.000 0.611 0.611 RIinat7 =~
trf97e5 1.000 0.588 0.588 trf97e7 1.000 0.588 0.588 trf97e10 1.000 0.588 0.588 trf97e12 1.000 0.588 0.588 RIinat8 =~
trf98e5 1.000 0.595 0.595 trf98e7 1.000 0.595 0.595 trf98e10 1.000 0.595 0.595 trf98e12 1.000 0.595 0.595 RIinat9 =~
trf99e5 1.000 0.588 0.588 trf99e7 1.000 0.588 0.588 trf99e10 1.000 0.588 0.588 trf99e12 1.000 0.588 0.588 RIsi1 =~
trf11e5 1.000 0.528 0.528 trf11e7 1.000 0.528 0.528 trf11e10 1.000 0.528 0.528 trf11e12 1.000 0.528 0.528 RIsi2 =~
trf19e5 1.000 0.592 0.592 trf19e7 1.000 0.592 0.592 trf19e10 1.000 0.592 0.592 trf19e12 1.000 0.592 0.592 RIsi3 =~
trf24e5 1.000 0.545 0.545 trf24e7 1.000 0.545 0.545 trf24e10 1.000 0.545 0.545 trf24e12 1.000 0.545 0.545 RIsi4 =~
trf30e5 1.000 0.556 0.556 trf30e7 1.000 0.556 0.556 trf30e10 1.000 0.556 0.556 trf30e12 1.000 0.556 0.556 RIsi5 =~
trf34e5 1.000 0.597 0.597 trf34e7 1.000 0.597 0.597 trf34e10 1.000 0.597 0.597 trf34e12 1.000 0.597 0.597 RIsi6 =~
trf77e5 1.000 0.558 0.558 trf77e7 1.000 0.558 0.558 trf77e10 1.000 0.558 0.558 trf77e12 1.000 0.558 0.558 WFinat5 =~
trf89e5 (a) 1.000 0.628 0.628 trf90e5 (b) 1.064 0.022 48.168 0.000 0.668 0.668 trf91e5 (c) 1.049 0.021 51.070 0.000 0.658 0.658 trf94e5 (d) 1.063 0.025 43.197 0.000 0.667 0.667 trf95e5 (e) 1.002 0.033 30.269 0.000 0.629 0.629 trf96e5 (f) 1.024 0.027 38.190 0.000 0.643 0.643 trf97e5 (g) 1.086 0.026 42.292 0.000 0.682 0.682 trf98e5 (h) 0.916 0.036 25.352 0.000 0.575 0.575 trf99e5 (i) 1.038 0.031 33.967 0.000 0.651 0.651 WFinat7 =~
trf89e7 (a) 1.000 0.632 0.632 trf90e7 (b) 1.064 0.022 48.168 0.000 0.673 0.673 trf91e7 (c) 1.049 0.021 51.070 0.000 0.663 0.663 trf94e7 (d) 1.063 0.025 43.197 0.000 0.672 0.672 trf95e7 (e) 1.002 0.033 30.269 0.000 0.634 0.634 trf96e7 (f) 1.024 0.027 38.190 0.000 0.648 0.648 trf97e7 (g) 1.086 0.026 42.292 0.000 0.687 0.687 trf98e7 (h) 0.916 0.036 25.352 0.000 0.579 0.579 trf99e7 (i) 1.038 0.031 33.967 0.000 0.656 0.656 WFinat10 =~
trf89e10 (a) 1.000 0.638 0.638 trf90e10 (b) 1.064 0.022 48.168 0.000 0.679 0.679 trf91e10 (c) 1.049 0.021 51.070 0.000 0.669 0.669 trf94e10 (d) 1.063 0.025 43.197 0.000 0.678 0.678 trf95e10 (e) 1.002 0.033 30.269 0.000 0.639 0.639 trf96e10 (f) 1.024 0.027 38.190 0.000 0.653 0.653 trf97e10 (g) 1.086 0.026 42.292 0.000 0.693 0.693 trf98e10 (h) 0.916 0.036 25.352 0.000 0.584 0.584 trf99e10 (i) 1.038 0.031 33.967 0.000 0.662 0.662 WFinat12 =~
trf89e12 (a) 1.000 0.654 0.654 trf90e12 (b) 1.064 0.022 48.168 0.000 0.696 0.696 trf91e12 (c) 1.049 0.021 51.070 0.000 0.686 0.686 trf94e12 (d) 1.063 0.025 43.197 0.000 0.695 0.695 trf95e12 (e) 1.002 0.033 30.269 0.000 0.655 0.655 trf96e12 (f) 1.024 0.027 38.190 0.000 0.670 0.670 trf97e12 (g) 1.086 0.026 42.292 0.000 0.710 0.710 trf98e12 (h) 0.916 0.036 25.352 0.000 0.599 0.599 trf99e12 (i) 1.038 0.031 33.967 0.000 0.678 0.678 WFsi5 =~
trf11e5 (j) 1.000 0.574 0.574 trf19e5 (k) 1.066 0.067 15.798 0.000 0.612 0.612 trf24e5 (l) 1.056 0.065 16.148 0.000 0.606 0.606 trf30e5 (m) 0.909 0.067 13.489 0.000 0.521 0.521 trf34e5 (n) 1.077 0.072 14.878 0.000 0.618 0.618 trf77e5 (o) 1.113 0.069 16.156 0.000 0.638 0.638 WFsi7 =~
trf11e7 (j) 1.000 0.592 0.592 trf19e7 (k) 1.066 0.067 15.798 0.000 0.631 0.631 trf24e7 (l) 1.056 0.065 16.148 0.000 0.625 0.625 trf30e7 (m) 0.909 0.067 13.489 0.000 0.538 0.538 trf34e7 (n) 1.077 0.072 14.878 0.000 0.637 0.637 trf77e7 (o) 1.113 0.069 16.156 0.000 0.659 0.659 WFsi10 =~
trf11e10 (j) 1.000 0.624 0.624 trf19e10 (k) 1.066 0.067 15.798 0.000 0.665 0.665 trf24e10 (l) 1.056 0.065 16.148 0.000 0.659 0.659 trf30e10 (m) 0.909 0.067 13.489 0.000 0.567 0.567 trf34e10 (n) 1.077 0.072 14.878 0.000 0.672 0.672 trf77e10 (o) 1.113 0.069 16.156 0.000 0.694 0.694 WFsi12 =~
trf11e12 (j) 1.000 0.626 0.626 trf19e12 (k) 1.066 0.067 15.798 0.000 0.667 0.667 trf24e12 (l) 1.056 0.065 16.148 0.000 0.661 0.661 trf30e12 (m) 0.909 0.067 13.489 0.000 0.569 0.569 trf34e12 (n) 1.077 0.072 14.878 0.000 0.674 0.674 trf77e12 (o) 1.113 0.069 16.156 0.000 0.697 0.697

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.367 0.056 6.510 0.000 0.364 0.364 WFsi5 -0.269 0.079 -3.418 0.001 -0.244 -0.244 WFsi7 ~
WFinat5 -0.119 0.059 -2.007 0.045 -0.126 -0.126 WFsi5 0.319 0.091 3.506 0.000 0.309 0.309 WFinat10 ~
WFinat7 0.366 0.065 5.622 0.000 0.363 0.363 WFsi7 -0.302 0.081 -3.744 0.000 -0.280 -0.280 WFsi10 ~
WFinat7 0.061 0.078 0.785 0.432 0.062 0.062 WFsi7 0.124 0.107 1.162 0.245 0.118 0.118 WFinat12 ~
WFinat10 0.286 0.068 4.236 0.000 0.279 0.279 WFsi10 -0.125 0.080 -1.569 0.117 -0.120 -0.120 WFsi12 ~
WFinat10 -0.069 0.072 -0.955 0.340 -0.070 -0.070 WFsi10 0.336 0.080 4.202 0.000 0.334 0.334

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.122 0.022 5.593 0.000 0.338 0.338 .WFinat7 ~~
.WFsi7 0.155 0.021 7.435 0.000 0.465 0.465 .WFinat10 ~~
.WFsi10 0.171 0.022 7.696 0.000 0.468 0.468 .WFinat12 ~~
.WFsi12 0.182 0.022 8.442 0.000 0.484 0.484 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.429 0.027 16.146 0.000 1.037 1.037 RIinat3 0.475 0.026 17.985 0.000 1.052 1.052 RIinat4 0.372 0.026 14.222 0.000 0.910 0.910 RIinat5 0.311 0.027 11.729 0.000 0.844 0.844 RIinat6 0.370 0.026 13.997 0.000 0.881 0.881 RIinat7 0.383 0.028 13.902 0.000 0.947 0.947 RIinat8 0.290 0.026 10.994 0.000 0.709 0.709 RIinat9 0.356 0.027 12.983 0.000 0.882 0.882 RIsi1 0.170 0.026 6.444 0.000 0.469 0.469 RIsi2 0.335 0.023 14.573 0.000 0.823 0.823 RIsi3 0.189 0.028 6.735 0.000 0.504 0.504 RIsi4 0.176 0.024 7.351 0.000 0.461 0.461 RIsi5 0.334 0.025 13.351 0.000 0.814 0.814 RIsi6 0.199 0.024 8.232 0.000 0.518 0.518 RIinat2 ~~
RIinat3 0.392 0.027 14.561 0.000 0.995 0.995 RIinat4 0.324 0.026 12.284 0.000 0.908 0.908 RIinat5 0.264 0.027 9.834 0.000 0.821 0.821 RIinat6 0.334 0.027 12.450 0.000 0.909 0.909 RIinat7 0.340 0.028 12.330 0.000 0.961 0.961 RIinat8 0.259 0.027 9.627 0.000 0.723 0.723 RIinat9 0.313 0.028 11.173 0.000 0.887 0.887 RIsi1 0.162 0.025 6.381 0.000 0.511 0.511 RIsi2 0.300 0.022 13.442 0.000 0.845 0.845 RIsi3 0.186 0.027 6.864 0.000 0.568 0.568 RIsi4 0.170 0.023 7.329 0.000 0.509 0.509 RIsi5 0.296 0.024 12.256 0.000 0.825 0.825 RIsi6 0.189 0.024 7.796 0.000 0.564 0.564 RIinat3 ~~
RIinat4 0.375 0.027 14.056 0.000 0.963 0.963 RIinat5 0.300 0.027 11.217 0.000 0.853 0.853 RIinat6 0.354 0.027 13.186 0.000 0.882 0.882 RIinat7 0.365 0.028 13.006 0.000 0.945 0.945 RIinat8 0.270 0.028 9.833 0.000 0.692 0.692 RIinat9 0.313 0.028 11.102 0.000 0.812 0.812 RIsi1 0.151 0.025 5.954 0.000 0.436 0.436 RIsi2 0.345 0.022 15.445 0.000 0.889 0.889 RIsi3 0.188 0.027 7.067 0.000 0.526 0.526 RIsi4 0.130 0.024 5.486 0.000 0.355 0.355 RIsi5 0.336 0.024 13.803 0.000 0.857 0.857 RIsi6 0.145 0.024 6.171 0.000 0.397 0.397 RIinat4 ~~
RIinat5 0.273 0.026 10.412 0.000 0.859 0.859 RIinat6 0.335 0.026 12.816 0.000 0.923 0.923 RIinat7 0.344 0.027 12.651 0.000 0.985 0.985 RIinat8 0.271 0.026 10.329 0.000 0.766 0.766 RIinat9 0.287 0.027 10.495 0.000 0.824 0.824 RIsi1 0.163 0.025 6.524 0.000 0.521 0.521 RIsi2 0.340 0.022 15.270 0.000 0.969 0.969 RIsi3 0.202 0.027 7.469 0.000 0.626 0.626 RIsi4 0.117 0.024 4.942 0.000 0.354 0.354 RIsi5 0.336 0.024 14.014 0.000 0.950 0.950 RIsi6 0.116 0.025 4.696 0.000 0.349 0.349 RIinat5 ~~
RIinat6 0.299 0.027 11.200 0.000 0.913 0.913 RIinat7 0.255 0.027 9.273 0.000 0.808 0.808 RIinat8 0.265 0.027 9.858 0.000 0.829 0.829 RIinat9 0.294 0.029 10.297 0.000 0.934 0.934 RIsi1 0.140 0.029 4.787 0.000 0.495 0.495 RIsi2 0.326 0.025 13.203 0.000 1.026 1.026 RIsi3 0.201 0.030 6.630 0.000 0.688 0.688 RIsi4 0.220 0.026 8.416 0.000 0.737 0.737 RIsi5 0.319 0.027 12.013 0.000 0.996 0.996 RIsi6 0.238 0.026 9.094 0.000 0.795 0.795 RIinat6 ~~
RIinat7 0.351 0.027 12.959 0.000 0.978 0.978 RIinat8 0.375 0.026 14.582 0.000 1.032 1.032 RIinat9 0.337 0.028 12.185 0.000 0.939 0.939 RIsi1 0.171 0.026 6.618 0.000 0.530 0.530 RIsi2 0.321 0.022 14.476 0.000 0.887 0.887 RIsi3 0.214 0.026 8.090 0.000 0.643 0.643 RIsi4 0.183 0.024 7.743 0.000 0.540 0.540 RIsi5 0.306 0.024 12.609 0.000 0.840 0.840 RIsi6 0.174 0.024 7.223 0.000 0.512 0.512 RIinat7 ~~
RIinat8 0.272 0.028 9.855 0.000 0.776 0.776 RIinat9 0.303 0.028 10.637 0.000 0.876 0.876 RIsi1 0.173 0.025 6.774 0.000 0.556 0.556 RIsi2 0.338 0.023 14.787 0.000 0.970 0.970 RIsi3 0.210 0.027 7.815 0.000 0.655 0.655 RIsi4 0.168 0.024 7.110 0.000 0.513 0.513 RIsi5 0.318 0.025 12.895 0.000 0.905 0.905 RIsi6 0.178 0.024 7.409 0.000 0.543 0.543 RIinat8 ~~
RIinat9 0.337 0.027 12.249 0.000 0.962 0.962 RIsi1 0.218 0.025 8.563 0.000 0.693 0.693 RIsi2 0.283 0.024 11.919 0.000 0.804 0.804 RIsi3 0.262 0.028 9.521 0.000 0.809 0.809 RIsi4 0.187 0.025 7.647 0.000 0.566 0.566 RIsi5 0.285 0.027 10.614 0.000 0.801 0.801 RIsi6 0.151 0.025 6.148 0.000 0.456 0.456 RIinat9 ~~
RIsi1 0.185 0.027 6.776 0.000 0.598 0.598 RIsi2 0.257 0.024 10.648 0.000 0.738 0.738 RIsi3 0.205 0.029 7.126 0.000 0.641 0.641 RIsi4 0.188 0.024 7.890 0.000 0.575 0.575 RIsi5 0.255 0.027 9.334 0.000 0.727 0.727 RIsi6 0.215 0.024 9.001 0.000 0.657 0.657 RIsi1 ~~
RIsi2 0.185 0.033 5.600 0.000 0.591 0.591 RIsi3 0.351 0.040 8.795 0.000 1.222 1.222 RIsi4 0.169 0.032 5.247 0.000 0.576 0.576 RIsi5 0.189 0.037 5.062 0.000 0.600 0.600 RIsi6 0.120 0.035 3.440 0.001 0.407 0.407 RIsi2 ~~
RIsi3 0.219 0.034 6.379 0.000 0.679 0.679 RIsi4 0.157 0.031 5.044 0.000 0.478 0.478 RIsi5 0.394 0.034 11.640 0.000 1.117 1.117 RIsi6 0.093 0.034 2.759 0.006 0.283 0.283 RIsi3 ~~
RIsi4 0.102 0.033 3.062 0.002 0.338 0.338 RIsi5 0.210 0.040 5.264 0.000 0.646 0.646 RIsi6 0.040 0.035 1.144 0.252 0.131 0.131 RIsi4 ~~
RIsi5 0.167 0.034 4.962 0.000 0.503 0.503 RIsi6 0.326 0.033 9.763 0.000 1.051 1.051 RIsi5 ~~
RIsi6 0.099 0.037 2.668 0.008 0.297 0.297

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf89e5 0.133 0.133 0.133 .trf89e7 0.127 0.127 0.127 .trf89e10 0.120 0.120 0.120 .trf89e12 0.100 0.100 0.100 .trf90e5 0.193 0.193 0.193 .trf90e7 0.186 0.186 0.186 .trf90e10 0.179 0.179 0.179 .trf90e12 0.155 0.155 0.155 .trf91e5 0.136 0.136 0.136 .trf91e7 0.129 0.129 0.129 .trf91e10 0.121 0.121 0.121 .trf91e12 0.099 0.099 0.099 .trf94e5 0.203 0.203 0.203 .trf94e7 0.196 0.196 0.196 .trf94e10 0.188 0.188 0.188 .trf94e12 0.165 0.165 0.165 .trf95e5 0.317 0.317 0.317 .trf95e7 0.311 0.311 0.311 .trf95e10 0.304 0.304 0.304 .trf95e12 0.283 0.283 0.283 .trf96e5 0.213 0.213 0.213 .trf96e7 0.207 0.207 0.207 .trf96e10 0.200 0.200 0.200 .trf96e12 0.178 0.178 0.178 .trf97e5 0.189 0.189 0.189 .trf97e7 0.182 0.182 0.182 .trf97e10 0.174 0.174 0.174 .trf97e12 0.150 0.150 0.150 .trf98e5 0.315 0.315 0.315 .trf98e7 0.310 0.310 0.310 .trf98e10 0.304 0.304 0.304 .trf98e12 0.287 0.287 0.287 .trf99e5 0.231 0.231 0.231 .trf99e7 0.224 0.224 0.224 .trf99e10 0.217 0.217 0.217 .trf99e12 0.195 0.195 0.195 .trf11e5 0.393 0.393 0.393 .trf11e7 0.371 0.371 0.371 .trf11e10 0.333 0.333 0.333 .trf11e12 0.330 0.330 0.330 .trf19e5 0.276 0.276 0.276 .trf19e7 0.252 0.252 0.252 .trf19e10 0.208 0.208 0.208 .trf19e12 0.205 0.205 0.205 .trf24e5 0.336 0.336 0.336 .trf24e7 0.313 0.313 0.313 .trf24e10 0.270 0.270 0.270 .trf24e12 0.266 0.266 0.266 .trf30e5 0.419 0.419 0.419 .trf30e7 0.401 0.401 0.401 .trf30e10 0.370 0.370 0.370 .trf30e12 0.367 0.367 0.367 .trf34e5 0.262 0.262 0.262 .trf34e7 0.238 0.238 0.238 .trf34e10 0.193 0.193 0.193 .trf34e12 0.190 0.190 0.190 .trf77e5 0.281 0.281 0.281 .trf77e7 0.255 0.255 0.255 .trf77e10 0.207 0.207 0.207 .trf77e12 0.204 0.204 0.204 RIinat1 0.473 0.027 17.305 0.000 1.000 1.000 RIinat2 0.361 0.029 12.528 0.000 1.000 1.000 RIinat3 0.431 0.028 15.353 0.000 1.000 1.000 RIinat4 0.352 0.028 12.712 0.000 1.000 1.000 RIinat5 0.287 0.032 9.059 0.000 1.000 1.000 RIinat6 0.373 0.028 13.283 0.000 1.000 1.000 RIinat7 0.346 0.031 11.330 0.000 1.000 1.000 RIinat8 0.354 0.031 11.538 0.000 1.000 1.000 RIinat9 0.345 0.032 10.657 0.000 1.000 1.000 RIsi1 0.278 0.045 6.192 0.000 1.000 1.000 RIsi2 0.350 0.033 10.505 0.000 1.000 1.000 RIsi3 0.297 0.047 6.356 0.000 1.000 1.000 RIsi4 0.309 0.035 8.719 0.000 1.000 1.000 RIsi5 0.356 0.039 9.022 0.000 1.000 1.000 RIsi6 0.311 0.037 8.345 0.000 1.000 1.000 WFinat5 0.394 0.027 14.718 0.000 1.000 1.000 .WFinat7 0.347 0.022 15.613 0.000 0.868 0.868 .WFinat10 0.350 0.022 15.590 0.000 0.859 0.859 .WFinat12 0.400 0.022 18.072 0.000 0.936 0.936 WFsi5 0.329 0.043 7.693 0.000 1.000 1.000 .WFsi7 0.321 0.036 8.834 0.000 0.915 0.915 .WFsi10 0.380 0.044 8.611 0.000 0.977 0.977 .WFsi12 0.354 0.040 8.752 0.000 0.903 0.903

S2 for the pairwise deletion model without robust standard errors: (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.995 (>0.95) Change in CFI: 0.000 (increase) - same fit Tucker-Lewis Index (TLI) 0.994 (>0.95) Change in TLI: 0.001 (decrease) - worse fit RMSEA 0.013 (≤ 0.06) Change in RMSEA: 0.001 (decrease) - worse fit 90 Percent confidence interval - lower 0.011 90 Percent confidence interval - upper 0.014
SRMR 0.052 (≤ 0.08) Change in SRMR: 0.001 (increase) - worse fit

# summary(semTools::compareFit(RICLPMt_multi_inat_S1.fit, RICLPMt_multi_inat_S2.fit, nested = TRUE)) #† indicates the best fitting model - using the robust statistics

Important to note that the Chi Square p value is very likely to be significant when using large samples. We should consider the change in model fit as well as the Chi square.

lavTestLRT(RICLPMt_multi_inat_S1.fit, RICLPMt_multi_inat_S2.fit)

Significantly worse fit to include the restrictions, p=0.000000163

Now we will move onto strong invariance tests.

RICLPMt_multi_inat_S3: Inattention step 3

Multiple response items RICLPMt teacher report inattention ADHD symptoms and social isolation: Step 3

Fitting a model with constraints to ensure strong factorial invariance, with a random intercept for each indicator separately.

RICLPMt_multi_inat_S3 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of inattention (teacher report)
  RIinat1 =~ 1*trf89e5 + 1*trf89e7 + 1*trf89e10 + 1*trf89e12
  RIinat2 =~ 1*trf90e5 + 1*trf90e7 + 1*trf90e10 + 1*trf90e12
  RIinat3 =~ 1*trf91e5 + 1*trf91e7 + 1*trf91e10 + 1*trf91e12
  RIinat4 =~ 1*trf94e5 + 1*trf94e7 + 1*trf94e10 + 1*trf94e12
  RIinat5 =~ 1*trf95e5 + 1*trf95e7 + 1*trf95e10 + 1*trf95e12
  RIinat6 =~ 1*trf96e5 + 1*trf96e7 + 1*trf96e10 + 1*trf96e12
  RIinat7 =~ 1*trf97e5 + 1*trf97e7 + 1*trf97e10 + 1*trf97e12
  RIinat8 =~ 1*trf98e5 + 1*trf98e7 + 1*trf98e10 + 1*trf98e12
  RIinat9 =~ 1*trf99e5 + 1*trf99e7 + 1*trf99e10 + 1*trf99e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  WFinat5 =~ a*trf89e5 + b*trf90e5 + c*trf91e5 + d*trf94e5 + e*trf95e5 + f*trf96e5 + g*trf97e5 + h*trf98e5 + i*trf99e5
  WFinat7 =~ a*trf89e7 + b*trf90e7 + c*trf91e7 + d*trf94e7 + e*trf95e7 + f*trf96e7 + g*trf97e7 + h*trf98e7 + i*trf99e7
  WFinat10 =~ a*trf89e10 + b*trf90e10 + c*trf91e10 + d*trf94e10 + e*trf95e10 + f*trf96e10 + g*trf97e10 + h*trf98e10 + i*trf99e10
  WFinat12 =~ a*trf89e12 + b*trf90e12 + c*trf91e12 + d*trf94e12 + e*trf95e12 + f*trf96e12 + g*trf97e12 + h*trf98e12 + i*trf99e12
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  WFsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  WFsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  WFsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  trf89e5 + trf89e7 + trf89e10 + trf89e12 ~ p*1
  trf90e5 + trf90e7 + trf90e10 + trf90e12 ~ q*1
  trf91e5 + trf91e7 + trf91e10 + trf91e12 ~ r*1
  trf94e5 + trf94e7 + trf94e10 + trf94e12 ~ s*1
  trf95e5 + trf95e7 + trf95e10 + trf95e12 ~ t*1
  trf96e5 + trf96e7 + trf96e10 + trf96e12 ~ u*1
  trf97e5 + trf97e7 + trf97e10 + trf97e12 ~ v*1
  trf98e5 + trf98e7 + trf98e10 + trf98e12 ~ w*1
  trf99e5 + trf99e7 + trf99e10 + trf99e12 ~ x*1
  
  trf11e5 + trf11e7 + trf11e10 + trf11e12 ~ y*1
  trf19e5 + trf19e7 + trf19e10 + trf19e12 ~ z*1
  trf24e5 + trf24e7 + trf24e10 + trf24e12 ~ aa*1
  trf30e5 + trf30e7 + trf30e10 + trf30e12 ~ ab*1
  trf34e5 + trf34e7 + trf34e10 + trf34e12 ~ ac*1
  trf77e5 + trf77e7 + trf77e10 + trf77e12 ~ ad*1
  
  # Free latent means from timepoint = 2 (age 7) onward. 
  # Only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  WFinat7 + WFinat10 + WFinat12 + WFsi7 + WFsi10 + WFsi12 ~ 1
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFinat5 ~~ WFsi5
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIinat1 + RIinat2 + RIinat3 + RIinat4 + RIinat5 + RIinat6 + RIinat7 + RIinat8 + RIinat9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFinat5
'

RICLPMt_multi_inat_S3.fit <- cfa(RICLPMt_multi_inat_S3, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise')

RICLPMt_multi_inat_S3.fit.summary <- summary(RICLPMt_multi_inat_S3.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 116 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 382 Number of equality constraints 84

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 279

Model Test User Model: Standard Robust Test Statistic 2344.331 2163.677 Degrees of freedom 1592 1592 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.048 Shift parameter 1018.805 simple second-order correction

Model Test Baseline Model:

Test statistic 451629.807 110523.928 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.136

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.998 0.995 Tucker-Lewis Index (TLI) 0.998 0.994

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.015 0.013 90 Percent confidence interval - lower 0.013 0.011 90 Percent confidence interval - upper 0.016 0.014 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.052 0.052

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIinat1 =~
trf89e5 1.000 0.688 0.688 trf89e7 1.000 0.688 0.688 trf89e10 1.000 0.688 0.688 trf89e12 1.000 0.688 0.688 RIinat2 =~
trf90e5 1.000 0.601 0.601 trf90e7 1.000 0.601 0.601 trf90e10 1.000 0.601 0.601 trf90e12 1.000 0.601 0.601 RIinat3 =~
trf91e5 1.000 0.657 0.657 trf91e7 1.000 0.657 0.657 trf91e10 1.000 0.657 0.657 trf91e12 1.000 0.657 0.657 RIinat4 =~
trf94e5 1.000 0.593 0.593 trf94e7 1.000 0.593 0.593 trf94e10 1.000 0.593 0.593 trf94e12 1.000 0.593 0.593 RIinat5 =~
trf95e5 1.000 0.536 0.536 trf95e7 1.000 0.536 0.536 trf95e10 1.000 0.536 0.536 trf95e12 1.000 0.536 0.536 RIinat6 =~
trf96e5 1.000 0.611 0.611 trf96e7 1.000 0.611 0.611 trf96e10 1.000 0.611 0.611 trf96e12 1.000 0.611 0.611 RIinat7 =~
trf97e5 1.000 0.588 0.588 trf97e7 1.000 0.588 0.588 trf97e10 1.000 0.588 0.588 trf97e12 1.000 0.588 0.588 RIinat8 =~
trf98e5 1.000 0.595 0.595 trf98e7 1.000 0.595 0.595 trf98e10 1.000 0.595 0.595 trf98e12 1.000 0.595 0.595 RIinat9 =~
trf99e5 1.000 0.588 0.588 trf99e7 1.000 0.588 0.588 trf99e10 1.000 0.588 0.588 trf99e12 1.000 0.588 0.588 RIsi1 =~
trf11e5 1.000 0.528 0.528 trf11e7 1.000 0.528 0.528 trf11e10 1.000 0.528 0.528 trf11e12 1.000 0.528 0.528 RIsi2 =~
trf19e5 1.000 0.592 0.592 trf19e7 1.000 0.592 0.592 trf19e10 1.000 0.592 0.592 trf19e12 1.000 0.592 0.592 RIsi3 =~
trf24e5 1.000 0.545 0.545 trf24e7 1.000 0.545 0.545 trf24e10 1.000 0.545 0.545 trf24e12 1.000 0.545 0.545 RIsi4 =~
trf30e5 1.000 0.556 0.556 trf30e7 1.000 0.556 0.556 trf30e10 1.000 0.556 0.556 trf30e12 1.000 0.556 0.556 RIsi5 =~
trf34e5 1.000 0.597 0.597 trf34e7 1.000 0.597 0.597 trf34e10 1.000 0.597 0.597 trf34e12 1.000 0.597 0.597 RIsi6 =~
trf77e5 1.000 0.558 0.558 trf77e7 1.000 0.558 0.558 trf77e10 1.000 0.558 0.558 trf77e12 1.000 0.558 0.558 WFinat5 =~
trf89e5 (a) 1.000 0.628 0.628 trf90e5 (b) 1.064 0.022 48.168 0.000 0.668 0.668 trf91e5 (c) 1.049 0.021 51.070 0.000 0.658 0.658 trf94e5 (d) 1.063 0.025 43.197 0.000 0.667 0.667 trf95e5 (e) 1.002 0.033 30.269 0.000 0.629 0.629 trf96e5 (f) 1.024 0.027 38.190 0.000 0.643 0.643 trf97e5 (g) 1.086 0.026 42.292 0.000 0.682 0.682 trf98e5 (h) 0.916 0.036 25.352 0.000 0.575 0.575 trf99e5 (i) 1.038 0.031 33.967 0.000 0.651 0.651 WFinat7 =~
trf89e7 (a) 1.000 0.632 0.632 trf90e7 (b) 1.064 0.022 48.168 0.000 0.673 0.673 trf91e7 (c) 1.049 0.021 51.070 0.000 0.663 0.663 trf94e7 (d) 1.063 0.025 43.197 0.000 0.672 0.672 trf95e7 (e) 1.002 0.033 30.269 0.000 0.634 0.634 trf96e7 (f) 1.024 0.027 38.190 0.000 0.648 0.648 trf97e7 (g) 1.086 0.026 42.292 0.000 0.687 0.687 trf98e7 (h) 0.916 0.036 25.352 0.000 0.579 0.579 trf99e7 (i) 1.038 0.031 33.967 0.000 0.656 0.656 WFinat10 =~
trf89e10 (a) 1.000 0.638 0.638 trf90e10 (b) 1.064 0.022 48.168 0.000 0.679 0.679 trf91e10 (c) 1.049 0.021 51.070 0.000 0.669 0.669 trf94e10 (d) 1.063 0.025 43.197 0.000 0.678 0.678 trf95e10 (e) 1.002 0.033 30.269 0.000 0.639 0.639 trf96e10 (f) 1.024 0.027 38.190 0.000 0.653 0.653 trf97e10 (g) 1.086 0.026 42.292 0.000 0.693 0.693 trf98e10 (h) 0.916 0.036 25.352 0.000 0.584 0.584 trf99e10 (i) 1.038 0.031 33.967 0.000 0.662 0.662 WFinat12 =~
trf89e12 (a) 1.000 0.654 0.654 trf90e12 (b) 1.064 0.022 48.168 0.000 0.696 0.696 trf91e12 (c) 1.049 0.021 51.070 0.000 0.686 0.686 trf94e12 (d) 1.063 0.025 43.197 0.000 0.695 0.695 trf95e12 (e) 1.002 0.033 30.269 0.000 0.655 0.655 trf96e12 (f) 1.024 0.027 38.190 0.000 0.670 0.670 trf97e12 (g) 1.086 0.026 42.292 0.000 0.710 0.710 trf98e12 (h) 0.916 0.036 25.352 0.000 0.599 0.599 trf99e12 (i) 1.038 0.031 33.967 0.000 0.678 0.678 WFsi5 =~
trf11e5 (j) 1.000 0.574 0.574 trf19e5 (k) 1.066 0.067 15.798 0.000 0.612 0.612 trf24e5 (l) 1.056 0.065 16.148 0.000 0.606 0.606 trf30e5 (m) 0.909 0.067 13.489 0.000 0.521 0.521 trf34e5 (n) 1.077 0.072 14.878 0.000 0.618 0.618 trf77e5 (o) 1.113 0.069 16.156 0.000 0.638 0.638 WFsi7 =~
trf11e7 (j) 1.000 0.592 0.592 trf19e7 (k) 1.066 0.067 15.798 0.000 0.631 0.631 trf24e7 (l) 1.056 0.065 16.148 0.000 0.625 0.625 trf30e7 (m) 0.909 0.067 13.489 0.000 0.538 0.538 trf34e7 (n) 1.077 0.072 14.878 0.000 0.637 0.637 trf77e7 (o) 1.113 0.069 16.156 0.000 0.659 0.659 WFsi10 =~
trf11e10 (j) 1.000 0.624 0.624 trf19e10 (k) 1.066 0.067 15.798 0.000 0.665 0.665 trf24e10 (l) 1.056 0.065 16.148 0.000 0.659 0.659 trf30e10 (m) 0.909 0.067 13.489 0.000 0.567 0.567 trf34e10 (n) 1.077 0.072 14.878 0.000 0.672 0.672 trf77e10 (o) 1.113 0.069 16.156 0.000 0.694 0.694 WFsi12 =~
trf11e12 (j) 1.000 0.626 0.626 trf19e12 (k) 1.066 0.067 15.798 0.000 0.667 0.667 trf24e12 (l) 1.056 0.065 16.148 0.000 0.661 0.661 trf30e12 (m) 0.909 0.067 13.489 0.000 0.569 0.569 trf34e12 (n) 1.077 0.072 14.878 0.000 0.674 0.674 trf77e12 (o) 1.113 0.069 16.156 0.000 0.697 0.697

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat5 ~~
WFsi5 0.122 0.022 5.593 0.000 0.338 0.338 .WFinat7 ~~
.WFsi7 0.155 0.021 7.435 0.000 0.465 0.465 .WFinat10 ~~
.WFsi10 0.171 0.022 7.696 0.000 0.468 0.468 .WFinat12 ~~
.WFsi12 0.182 0.022 8.442 0.000 0.484 0.484 RIinat1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat7 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat8 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat9 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 RIinat1 ~~
RIinat2 0.429 0.027 16.146 0.000 1.037 1.037 RIinat3 0.475 0.026 17.985 0.000 1.052 1.052 RIinat4 0.372 0.026 14.222 0.000 0.910 0.910 RIinat5 0.311 0.027 11.729 0.000 0.844 0.844 RIinat6 0.370 0.026 13.997 0.000 0.881 0.881 RIinat7 0.383 0.028 13.902 0.000 0.947 0.947 RIinat8 0.290 0.026 10.994 0.000 0.709 0.709 RIinat9 0.356 0.027 12.983 0.000 0.882 0.882 RIsi1 0.170 0.026 6.444 0.000 0.469 0.469 RIsi2 0.335 0.023 14.573 0.000 0.823 0.823 RIsi3 0.189 0.028 6.735 0.000 0.504 0.504 RIsi4 0.176 0.024 7.351 0.000 0.461 0.461 RIsi5 0.334 0.025 13.351 0.000 0.814 0.814 RIsi6 0.199 0.024 8.232 0.000 0.518 0.518 RIinat2 ~~
RIinat3 0.392 0.027 14.561 0.000 0.995 0.995 RIinat4 0.324 0.026 12.284 0.000 0.908 0.908 RIinat5 0.264 0.027 9.834 0.000 0.821 0.821 RIinat6 0.334 0.027 12.450 0.000 0.909 0.909 RIinat7 0.340 0.028 12.330 0.000 0.961 0.961 RIinat8 0.259 0.027 9.627 0.000 0.723 0.723 RIinat9 0.313 0.028 11.173 0.000 0.887 0.887 RIsi1 0.162 0.025 6.381 0.000 0.511 0.511 RIsi2 0.300 0.022 13.442 0.000 0.845 0.845 RIsi3 0.186 0.027 6.864 0.000 0.568 0.568 RIsi4 0.170 0.023 7.329 0.000 0.509 0.509 RIsi5 0.296 0.024 12.256 0.000 0.825 0.825 RIsi6 0.189 0.024 7.796 0.000 0.564 0.564 RIinat3 ~~
RIinat4 0.375 0.027 14.056 0.000 0.963 0.963 RIinat5 0.300 0.027 11.217 0.000 0.853 0.853 RIinat6 0.354 0.027 13.186 0.000 0.882 0.882 RIinat7 0.365 0.028 13.006 0.000 0.945 0.945 RIinat8 0.270 0.028 9.833 0.000 0.692 0.692 RIinat9 0.313 0.028 11.102 0.000 0.812 0.812 RIsi1 0.151 0.025 5.954 0.000 0.436 0.436 RIsi2 0.345 0.022 15.445 0.000 0.889 0.889 RIsi3 0.188 0.027 7.068 0.000 0.526 0.526 RIsi4 0.130 0.024 5.486 0.000 0.355 0.355 RIsi5 0.336 0.024 13.803 0.000 0.857 0.857 RIsi6 0.145 0.024 6.171 0.000 0.397 0.397 RIinat4 ~~
RIinat5 0.273 0.026 10.412 0.000 0.859 0.859 RIinat6 0.335 0.026 12.816 0.000 0.923 0.923 RIinat7 0.344 0.027 12.651 0.000 0.985 0.985 RIinat8 0.271 0.026 10.329 0.000 0.766 0.766 RIinat9 0.287 0.027 10.495 0.000 0.824 0.824 RIsi1 0.163 0.025 6.524 0.000 0.521 0.521 RIsi2 0.340 0.022 15.269 0.000 0.969 0.969 RIsi3 0.202 0.027 7.469 0.000 0.626 0.626 RIsi4 0.117 0.024 4.942 0.000 0.354 0.354 RIsi5 0.336 0.024 14.014 0.000 0.950 0.950 RIsi6 0.116 0.025 4.696 0.000 0.349 0.349 RIinat5 ~~
RIinat6 0.299 0.027 11.200 0.000 0.913 0.913 RIinat7 0.255 0.027 9.273 0.000 0.808 0.808 RIinat8 0.265 0.027 9.858 0.000 0.829 0.829 RIinat9 0.294 0.029 10.297 0.000 0.934 0.934 RIsi1 0.140 0.029 4.787 0.000 0.495 0.495 RIsi2 0.326 0.025 13.203 0.000 1.026 1.026 RIsi3 0.201 0.030 6.630 0.000 0.688 0.688 RIsi4 0.220 0.026 8.416 0.000 0.737 0.737 RIsi5 0.319 0.027 12.013 0.000 0.996 0.996 RIsi6 0.238 0.026 9.094 0.000 0.795 0.795 RIinat6 ~~
RIinat7 0.351 0.027 12.959 0.000 0.978 0.978 RIinat8 0.375 0.026 14.582 0.000 1.032 1.032 RIinat9 0.337 0.028 12.185 0.000 0.939 0.939 RIsi1 0.171 0.026 6.618 0.000 0.530 0.530 RIsi2 0.321 0.022 14.476 0.000 0.887 0.887 RIsi3 0.214 0.026 8.090 0.000 0.643 0.643 RIsi4 0.183 0.024 7.743 0.000 0.540 0.540 RIsi5 0.306 0.024 12.609 0.000 0.840 0.840 RIsi6 0.174 0.024 7.223 0.000 0.512 0.512 RIinat7 ~~
RIinat8 0.272 0.028 9.855 0.000 0.776 0.776 RIinat9 0.303 0.028 10.637 0.000 0.876 0.876 RIsi1 0.173 0.025 6.774 0.000 0.556 0.556 RIsi2 0.338 0.023 14.787 0.000 0.970 0.970 RIsi3 0.210 0.027 7.815 0.000 0.655 0.655 RIsi4 0.168 0.024 7.110 0.000 0.513 0.513 RIsi5 0.318 0.025 12.895 0.000 0.905 0.905 RIsi6 0.178 0.024 7.409 0.000 0.543 0.543 RIinat8 ~~
RIinat9 0.337 0.027 12.249 0.000 0.962 0.962 RIsi1 0.218 0.025 8.563 0.000 0.693 0.693 RIsi2 0.283 0.024 11.919 0.000 0.804 0.804 RIsi3 0.262 0.028 9.521 0.000 0.809 0.809 RIsi4 0.187 0.025 7.647 0.000 0.566 0.566 RIsi5 0.285 0.027 10.614 0.000 0.801 0.801 RIsi6 0.151 0.025 6.148 0.000 0.456 0.456 RIinat9 ~~
RIsi1 0.185 0.027 6.776 0.000 0.598 0.598 RIsi2 0.257 0.024 10.648 0.000 0.738 0.738 RIsi3 0.205 0.029 7.126 0.000 0.641 0.641 RIsi4 0.188 0.024 7.890 0.000 0.575 0.575 RIsi5 0.255 0.027 9.334 0.000 0.727 0.727 RIsi6 0.215 0.024 9.000 0.000 0.657 0.657 RIsi1 ~~
RIsi2 0.185 0.033 5.600 0.000 0.591 0.591 RIsi3 0.351 0.040 8.796 0.000 1.222 1.222 RIsi4 0.169 0.032 5.247 0.000 0.576 0.576 RIsi5 0.189 0.037 5.062 0.000 0.600 0.600 RIsi6 0.120 0.035 3.440 0.001 0.407 0.407 RIsi2 ~~
RIsi3 0.219 0.034 6.379 0.000 0.679 0.679 RIsi4 0.157 0.031 5.044 0.000 0.478 0.478 RIsi5 0.394 0.034 11.640 0.000 1.117 1.117 RIsi6 0.093 0.034 2.759 0.006 0.283 0.283 RIsi3 ~~
RIsi4 0.102 0.033 3.062 0.002 0.338 0.338 RIsi5 0.210 0.040 5.264 0.000 0.646 0.646 RIsi6 0.040 0.035 1.144 0.252 0.131 0.131 RIsi4 ~~
RIsi5 0.167 0.034 4.962 0.000 0.503 0.503 RIsi6 0.326 0.033 9.763 0.000 1.051 1.051 RIsi5 ~~
RIsi6 0.099 0.037 2.668 0.008 0.297 0.297

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf89e5 (p) -0.010 0.011 -0.933 0.351 -0.010 -0.010 .trf89e7 (p) -0.010 0.011 -0.933 0.351 -0.010 -0.010 .trf89e10 (p) -0.010 0.011 -0.933 0.351 -0.010 -0.010 .trf89e12 (p) -0.010 0.011 -0.933 0.351 -0.010 -0.010 .trf90e5 (q) -0.021 0.011 -1.896 0.058 -0.021 -0.021 .trf90e7 (q) -0.021 0.011 -1.896 0.058 -0.021 -0.021 .trf90e10 (q) -0.021 0.011 -1.896 0.058 -0.021 -0.021 .trf90e12 (q) -0.021 0.011 -1.896 0.058 -0.021 -0.021 .trf91e5 (r) 0.003 0.010 0.250 0.803 0.003 0.003 .trf91e7 (r) 0.003 0.010 0.250 0.803 0.003 0.003 .trf91e10 (r) 0.003 0.010 0.250 0.803 0.003 0.003 .trf91e12 (r) 0.003 0.010 0.250 0.803 0.003 0.003 .trf94e5 (s) 0.026 0.010 2.582 0.010 0.026 0.026 .trf94e7 (s) 0.026 0.010 2.582 0.010 0.026 0.026 .trf94e10 (s) 0.026 0.010 2.582 0.010 0.026 0.026 .trf94e12 (s) 0.026 0.010 2.582 0.010 0.026 0.026 .trf95e5 (t) -0.016 0.015 -1.092 0.275 -0.016 -0.016 .trf95e7 (t) -0.016 0.015 -1.092 0.275 -0.016 -0.016 .trf95e10 (t) -0.016 0.015 -1.092 0.275 -0.016 -0.016 .trf95e12 (t) -0.016 0.015 -1.092 0.275 -0.016 -0.016 .trf96e5 (u) -0.011 0.011 -1.001 0.317 -0.011 -0.011 .trf96e7 (u) -0.011 0.011 -1.001 0.317 -0.011 -0.011 .trf96e10 (u) -0.011 0.011 -1.001 0.317 -0.011 -0.011 .trf96e12 (u) -0.011 0.011 -1.001 0.317 -0.011 -0.011 .trf97e5 (v) -0.028 0.011 -2.664 0.008 -0.028 -0.028 .trf97e7 (v) -0.028 0.011 -2.664 0.008 -0.028 -0.028 .trf97e10 (v) -0.028 0.011 -2.664 0.008 -0.028 -0.028 .trf97e12 (v) -0.028 0.011 -2.664 0.008 -0.028 -0.028 .trf98e5 (w) -0.007 0.015 -0.464 0.643 -0.007 -0.007 .trf98e7 (w) -0.007 0.015 -0.464 0.643 -0.007 -0.007 .trf98e10 (w) -0.007 0.015 -0.464 0.643 -0.007 -0.007 .trf98e12 (w) -0.007 0.015 -0.464 0.643 -0.007 -0.007 .trf99e5 (x) -0.003 0.013 -0.202 0.840 -0.003 -0.003 .trf99e7 (x) -0.003 0.013 -0.202 0.840 -0.003 -0.003 .trf99e10 (x) -0.003 0.013 -0.202 0.840 -0.003 -0.003 .trf99e12 (x) -0.003 0.013 -0.202 0.840 -0.003 -0.003 .trf11e5 (y) 0.068 0.019 3.598 0.000 0.068 0.068 .trf11e7 (y) 0.068 0.019 3.598 0.000 0.068 0.068 .trf11e10 (y) 0.068 0.019 3.598 0.000 0.068 0.068 .trf11e12 (y) 0.068 0.019 3.598 0.000 0.068 0.068 .trf19e5 (z) 0.082 0.015 5.304 0.000 0.082 0.082 .trf19e7 (z) 0.082 0.015 5.304 0.000 0.082 0.082 .trf19e10 (z) 0.082 0.015 5.304 0.000 0.082 0.082 .trf19e12 (z) 0.082 0.015 5.304 0.000 0.082 0.082 .trf24e5 (aa) 0.075 0.023 3.336 0.001 0.075 0.075 .trf24e7 (aa) 0.075 0.023 3.336 0.001 0.075 0.075 .trf24e10 (aa) 0.075 0.023 3.336 0.001 0.075 0.075 .trf24e12 (aa) 0.075 0.023 3.336 0.001 0.075 0.075 .trf30e5 (ab) 0.081 0.018 4.484 0.000 0.081 0.081 .trf30e7 (ab) 0.081 0.018 4.484 0.000 0.081 0.081 .trf30e10 (ab) 0.081 0.018 4.484 0.000 0.081 0.081 .trf30e12 (ab) 0.081 0.018 4.484 0.000 0.081 0.081 .trf34e5 (ac) 0.037 0.023 1.602 0.109 0.037 0.037 .trf34e7 (ac) 0.037 0.023 1.602 0.109 0.037 0.037 .trf34e10 (ac) 0.037 0.023 1.602 0.109 0.037 0.037 .trf34e12 (ac) 0.037 0.023 1.602 0.109 0.037 0.037 .trf77e5 (ad) 0.020 0.016 1.218 0.223 0.020 0.020 .trf77e7 (ad) 0.020 0.016 1.218 0.223 0.020 0.020 .trf77e10 (ad) 0.020 0.016 1.218 0.223 0.020 0.020 .trf77e12 (ad) 0.020 0.016 1.218 0.223 0.020 0.020 .WFinat7 0.005 0.021 0.247 0.805 0.008 0.008 .WFinat10 -0.010 0.024 -0.388 0.698 -0.015 -0.015 .WFinat12 -0.001 0.026 -0.048 0.962 -0.002 -0.002 .WFsi7 -0.048 0.032 -1.494 0.135 -0.081 -0.081 .WFsi10 -0.043 0.027 -1.589 0.112 -0.070 -0.070 .WFsi12 -0.033 0.030 -1.081 0.280 -0.052 -0.052 RIinat1 0.000 0.000 0.000 RIinat2 0.000 0.000 0.000 RIinat3 0.000 0.000 0.000 RIinat4 0.000 0.000 0.000 RIinat5 0.000 0.000 0.000 RIinat6 0.000 0.000 0.000 RIinat7 0.000 0.000 0.000 RIinat8 0.000 0.000 0.000 RIinat9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf89e5|t1 0.452 0.023 19.972 0.000 0.452 0.452 trf89e5|t2 1.294 0.031 42.236 0.000 1.294 1.294 trf89e7|t1 0.601 0.022 27.353 0.000 0.601 0.601 trf89e7|t2 1.446 0.027 53.037 0.000 1.446 1.446 trf89e10|t1 0.716 0.024 29.576 0.000 0.716 0.716 trf89e10|t2 1.592 0.031 51.609 0.000 1.592 1.592 trf89e12|t1 0.769 0.025 30.583 0.000 0.769 0.769 trf89e12|t2 1.705 0.036 47.201 0.000 1.705 1.705 trf90e5|t1 0.524 0.024 22.196 0.000 0.524 0.524 trf90e5|t2 1.559 0.038 41.383 0.000 1.559 1.559 trf90e7|t1 0.569 0.023 24.705 0.000 0.569 0.569 trf90e7|t2 1.523 0.031 49.529 0.000 1.523 1.523 trf90e10|t1 0.658 0.025 26.222 0.000 0.658 0.658 trf90e10|t2 1.729 0.036 47.943 0.000 1.729 1.729 trf90e12|t1 0.719 0.027 26.641 0.000 0.719 0.719 trf90e12|t2 1.847 0.041 44.712 0.000 1.847 1.847 trf91e5|t1 0.244 0.022 10.889 0.000 0.244 0.244 trf91e5|t2 1.297 0.031 41.517 0.000 1.297 1.297 trf91e7|t1 0.315 0.022 14.004 0.000 0.315 0.315 trf91e7|t2 1.363 0.024 56.475 0.000 1.363 1.363 trf91e10|t1 0.396 0.025 16.001 0.000 0.396 0.396 trf91e10|t2 1.494 0.028 53.938 0.000 1.494 1.494 trf91e12|t1 0.458 0.026 17.295 0.000 0.458 0.458 trf91e12|t2 1.593 0.031 51.840 0.000 1.593 1.593 trf94e5|t1 0.411 0.024 17.437 0.000 0.411 0.411 trf94e5|t2 1.539 0.036 42.438 0.000 1.539 1.539 trf94e7|t1 0.433 0.023 18.951 0.000 0.433 0.433 trf94e7|t2 1.546 0.030 51.490 0.000 1.546 1.546 trf94e10|t1 0.441 0.025 17.544 0.000 0.441 0.441 trf94e10|t2 1.613 0.031 51.463 0.000 1.613 1.613 trf94e12|t1 0.560 0.027 20.975 0.000 0.560 0.560 trf94e12|t2 1.752 0.036 48.935 0.000 1.752 1.752 trf95e5|t1 0.950 0.029 33.300 0.000 0.950 0.950 trf95e5|t2 1.937 0.050 38.350 0.000 1.937 1.937 trf95e7|t1 1.100 0.027 40.647 0.000 1.100 1.100 trf95e7|t2 2.134 0.057 37.324 0.000 2.134 2.134 trf95e10|t1 1.168 0.028 41.245 0.000 1.168 1.168 trf95e10|t2 2.200 0.062 35.451 0.000 2.200 2.200 trf95e12|t1 1.063 0.030 35.274 0.000 1.063 1.063 trf95e12|t2 2.289 0.071 32.188 0.000 2.289 2.289 trf96e5|t1 0.556 0.025 22.620 0.000 0.556 0.556 trf96e5|t2 1.648 0.040 41.174 0.000 1.648 1.648 trf96e7|t1 0.468 0.023 20.365 0.000 0.468 0.468 trf96e7|t2 1.624 0.032 51.039 0.000 1.624 1.624 trf96e10|t1 0.632 0.024 26.757 0.000 0.632 0.632 trf96e10|t2 1.590 0.032 49.741 0.000 1.590 1.590 trf96e12|t1 0.629 0.026 24.372 0.000 0.629 0.629 trf96e12|t2 1.613 0.034 47.962 0.000 1.613 1.613 trf97e5|t1 0.407 0.024 17.209 0.000 0.407 0.407 trf97e5|t2 1.528 0.037 41.159 0.000 1.528 1.528 trf97e7|t1 0.503 0.023 21.985 0.000 0.503 0.503 trf97e7|t2 1.524 0.029 52.408 0.000 1.524 1.524 trf97e10|t1 0.730 0.024 30.472 0.000 0.730 0.730 trf97e10|t2 1.628 0.032 50.108 0.000 1.628 1.628 trf97e12|t1 0.818 0.026 31.716 0.000 0.818 0.818 trf97e12|t2 1.688 0.036 47.201 0.000 1.688 1.688 trf98e5|t1 0.968 0.028 34.253 0.000 0.968 0.968 trf98e5|t2 1.942 0.051 38.135 0.000 1.942 1.942 trf98e7|t1 1.046 0.027 38.173 0.000 1.046 1.046 trf98e7|t2 2.002 0.050 40.146 0.000 2.002 2.002 trf98e10|t1 1.196 0.028 42.964 0.000 1.196 1.196 trf98e10|t2 1.904 0.045 42.002 0.000 1.904 1.904 trf98e12|t1 1.338 0.031 43.307 0.000 1.338 1.338 trf98e12|t2 2.056 0.055 37.102 0.000 2.056 2.056 trf99e5|t1 0.840 0.026 31.937 0.000 0.840 0.840 trf99e5|t2 1.941 0.051 38.412 0.000 1.941 1.941 trf99e7|t1 0.793 0.025 32.084 0.000 0.793 0.793 trf99e7|t2 1.865 0.040 46.686 0.000 1.865 1.865 trf99e10|t1 1.079 0.026 41.221 0.000 1.079 1.079 trf99e10|t2 2.029 0.047 42.770 0.000 2.029 2.029 trf99e12|t1 1.137 0.028 40.045 0.000 1.137 1.137 trf99e12|t2 2.054 0.052 39.464 0.000 2.054 2.054 trf11e5|t1 1.599 0.038 42.249 0.000 1.599 1.599 trf11e5|t2 2.624 0.094 28.069 0.000 2.624 2.624 trf11e7|t1 1.370 0.038 35.716 0.000 1.370 1.370 trf11e7|t2 2.370 0.064 37.288 0.000 2.370 2.370 trf11e10|t1 1.444 0.033 43.121 0.000 1.444 1.444 trf11e10|t2 2.200 0.060 36.606 0.000 2.200 2.200 trf11e12|t1 1.539 0.038 40.029 0.000 1.539 1.539 trf11e12|t2 2.550 0.093 27.494 0.000 2.550 2.550 trf19e5|t1 1.223 0.030 40.422 0.000 1.223 1.223 trf19e5|t2 2.269 0.062 36.637 0.000 2.269 2.269 trf19e7|t1 1.001 0.036 28.106 0.000 1.001 1.001 trf19e7|t2 2.261 0.059 38.137 0.000 2.261 2.261 trf19e10|t1 0.902 0.030 29.829 0.000 0.902 0.902 trf19e10|t2 2.046 0.048 42.427 0.000 2.046 2.046 trf19e12|t1 0.920 0.032 28.505 0.000 0.920 0.920 trf19e12|t2 2.113 0.056 37.757 0.000 2.113 2.113 trf24e5|t1 1.820 0.043 42.639 0.000 1.820 1.820 trf24e5|t2 2.700 0.099 27.315 0.000 2.700 2.700 trf24e7|t1 1.583 0.043 37.202 0.000 1.583 1.583 trf24e7|t2 2.485 0.070 35.330 0.000 2.485 2.485 trf24e10|t1 1.604 0.039 40.843 0.000 1.604 1.604 trf24e10|t2 2.491 0.080 31.227 0.000 2.491 2.491 trf24e12|t1 1.671 0.045 36.775 0.000 1.671 1.671 trf24e12|t2 2.671 0.105 25.333 0.000 2.671 2.671 trf30e5|t1 1.200 0.030 39.459 0.000 1.200 1.200 trf30e5|t2 2.185 0.057 38.500 0.000 2.185 2.185 trf30e7|t1 1.298 0.037 34.728 0.000 1.298 1.298 trf30e7|t2 2.332 0.068 34.214 0.000 2.332 2.332 trf30e10|t1 1.342 0.034 39.827 0.000 1.342 1.342 trf30e10|t2 2.329 0.069 33.810 0.000 2.329 2.329 trf30e12|t1 1.133 0.034 33.536 0.000 1.133 1.133 trf30e12|t2 2.163 0.059 36.376 0.000 2.163 2.163 trf34e5|t1 1.759 0.043 40.840 0.000 1.759 1.759 trf34e5|t2 2.664 0.099 27.033 0.000 2.664 2.664 trf34e7|t1 1.626 0.048 33.975 0.000 1.626 1.626 trf34e7|t2 2.957 0.143 20.660 0.000 2.957 2.957 trf34e10|t1 1.252 0.036 34.636 0.000 1.252 1.252 trf34e10|t2 2.296 0.070 33.032 0.000 2.296 2.296 trf34e12|t1 1.250 0.038 33.271 0.000 1.250 1.250 trf34e12|t2 2.451 0.084 29.070 0.000 2.451 2.451 trf77e5|t1 1.078 0.029 36.845 0.000 1.078 1.078 trf77e5|t2 2.080 0.055 37.903 0.000 2.080 2.080 trf77e7|t1 1.150 0.037 30.845 0.000 1.150 1.150 trf77e7|t2 2.197 0.060 36.825 0.000 2.197 2.197 trf77e10|t1 1.174 0.032 36.625 0.000 1.174 1.174 trf77e10|t2 2.159 0.060 35.924 0.000 2.159 2.159 trf77e12|t1 1.004 0.034 29.267 0.000 1.004 1.004 trf77e12|t2 1.942 0.050 38.822 0.000 1.942 1.942

S3 Model fit (no robust se): (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.995 (>0.95) Change in CFI: 0.000 (decrease) Tucker-Lewis Index (TLI) 0.994 (>0.95) Change in TLI: 0.000 (decrease) RMSEA 0.013 (≤ 0.06) Change in RMSEA: 0.000 (increase) 90 Percent confidence interval - lower 0.011 90 Percent confidence interval - upper 0.014
SRMR 0.052 (≤ 0.08) Change in SRMR: 0.000

#lavTestLRT(RICLPMt_multi_inat_S2.fit, RICLPMt_multi_inat_S3.fit)

# summary(semTools::compareFit(RICLPMt_multi_inat_S2.fit, RICLPMt_multi_inat_S3.fit, nested = TRUE)) #† indicates the best fitting model - have hashed out here

Our model does not loose any fit, which means we can assume that strong factorial invariance holds over time. In contrast, If the overall model fit is not significantly worse in the strong invariance model compared to the weak invariance model, it indicates that constraining the item intercepts across time points does not significantly affect the model fit, and strong invariance is supported.

Going forward, we will assume that we have strong invariance

RICLPMt_multi_inat_S4: Inattention step 4 - Full model

Multiple indicator RI-CLPM, 4 waves with 9 indicators for inattention and 6 indicators for social isolation. Fitting a model with constraints to ensure strong factorial invariance, with the RI-CLPM at the latent level.

RICLPMt_multi_inat_S4 <- '
  #####################
  # MEASUREMENT MODEL #
  #####################
  
  # Factor models for inattention symptoms at 4 waves (constrained)
  Finat5 =~ a*trf89e5 + b*trf90e5 + c*trf91e5 + d*trf94e5 + e*trf95e5 + f*trf96e5 + g*trf97e5 + h*trf98e5 + i*trf99e5
  Finat7 =~ a*trf89e7 + b*trf90e7 + c*trf91e7 + d*trf94e7 + e*trf95e7 + f*trf96e7 + g*trf97e7 + h*trf98e7 + i*trf99e7
  Finat10 =~ a*trf89e10 + b*trf90e10 + c*trf91e10 + d*trf94e10 + e*trf95e10 + f*trf96e10 + g*trf97e10 + h*trf98e10 + i*trf99e10
  Finat12 =~ a*trf89e12 + b*trf90e12 + c*trf91e12 + d*trf94e12 + e*trf95e12 + f*trf96e12 + g*trf97e12 + h*trf98e12 + i*trf99e12
  
  # Factor models for social isolation at 4 waves (constrained)
  Fsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  Fsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  Fsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  Fsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  trf89e5 + trf89e7 + trf89e10 + trf89e12 ~ p*1
  trf90e5 + trf90e7 + trf90e10 + trf90e12 ~ q*1
  trf91e5 + trf91e7 + trf91e10 + trf91e12 ~ r*1
  trf94e5 + trf94e7 + trf94e10 + trf94e12 ~ s*1
  trf95e5 + trf95e7 + trf95e10 + trf95e12 ~ t*1
  trf96e5 + trf96e7 + trf96e10 + trf96e12 ~ u*1
  trf97e5 + trf97e7 + trf97e10 + trf97e12 ~ v*1
  trf98e5 + trf98e7 + trf98e10 + trf98e12 ~ w*1
  trf99e5 + trf99e7 + trf99e10 + trf99e12 ~ x*1
  
  trf11e5 + trf11e7 + trf11e10 + trf11e12 ~ y*1
  trf19e5 + trf19e7 + trf19e10 + trf19e12 ~ z*1
  trf24e5 + trf24e7 + trf24e10 + trf24e12 ~ aa*1
  trf30e5 + trf30e7 + trf30e10 + trf30e12 ~ ab*1
  trf34e5 + trf34e7 + trf34e10 + trf34e12 ~ ac*1
  trf77e5 + trf77e7 + trf77e10 + trf77e12 ~ ad*1
  
  # Free latent means from timepoint = 2 (age 7) onward. 
  # Only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  Finat7 + Finat10 + Finat12 + Fsi7 + Fsi10 + Fsi12 ~ 1
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts). 
  RIinat =~ 1*Finat5 + 1*Finat7 + 1*Finat10 + 1*Finat12 
  RIsi =~ 1*Fsi5 + 1*Fsi7 + 1*Fsi10 + 1*Fsi12
  
  # Set the residual variances of all Finat and Fsi variables to 0. 
  Finat5 ~~ 0*Finat5
  Finat7 ~~ 0*Finat7
  Finat10 ~~ 0*Finat10
  Finat12 ~~ 0*Finat12
  Fsi5 ~~ 0*Fsi5
  Fsi7 ~~ 0*Fsi7
  Fsi10 ~~ 0*Fsi10
  Fsi12 ~~ 0*Fsi12

  ###############
  # WITHIN PART #
  ###############
 
  # Create the within-part
  WFinat5 =~ 1*Finat5
  WFinat7 =~ 1*Finat7
  WFinat10 =~ 1*Finat10
  WFinat12 =~ 1*Finat12
  
  WFsi5 =~ 1*Fsi5
  WFsi7 =~ 1*Fsi7
  WFsi10 =~ 1*Fsi10
  WFsi12 =~ 1*Fsi12

  # Specify the lagged effects between the within-person centered latent variables
  WFinat7 + WFsi7 ~ WFinat5 + WFsi5
  WFinat10 + WFsi10 ~ WFinat7 + WFsi7
  WFinat12 + WFsi12 ~ WFinat10 + WFsi10
  
  # Estimate the correlations within the same wave - age 5 is missing here. 
  WFinat7 ~~ WFsi7
  WFinat10 ~~ WFsi10 
  WFinat12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Set correlations between the between-factors (random intercepts) and within-factors at wave 1 at 0. 
  RIinat + RIsi ~~ 0*WFinat5 + 0*WFsi5
'

RICLPMt_multi_inat_S4.fit <- cfa(RICLPMt_multi_inat_S4, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise')

RICLPM_multi_inat_S4.fit.summary <- summary(RICLPMt_multi_inat_S4.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 76 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 265 Number of equality constraints 84

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 279

Model Test User Model: Standard Robust Test Statistic 5098.118 3472.908 Degrees of freedom 1709 1709 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.149 Shift parameter 1100.238 simple second-order correction

Model Test Baseline Model:

Test statistic 451629.807 110523.928 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.136

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.992 0.984 Tucker-Lewis Index (TLI) 0.992 0.983

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.030 0.022 90 Percent confidence interval - lower 0.029 0.021 90 Percent confidence interval - upper 0.031 0.023 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.070 0.070

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Finat5 =~
trf89e5 (a) 1.000 0.940 0.940 trf90e5 (b) 0.947 0.006 162.064 0.000 0.889 0.889 trf91e5 (c) 0.989 0.006 178.433 0.000 0.930 0.930 trf94e5 (d) 0.925 0.007 136.237 0.000 0.869 0.869 trf95e5 (e) 0.838 0.011 73.314 0.000 0.788 0.788 trf96e5 (f) 0.932 0.007 134.099 0.000 0.875 0.875 trf97e5 (g) 0.943 0.006 148.395 0.000 0.886 0.886 trf98e5 (h) 0.825 0.012 66.459 0.000 0.775 0.775 trf99e5 (i) 0.894 0.009 103.014 0.000 0.840 0.840 Finat7 =~
trf89e7 (a) 1.000 0.943 0.943 trf90e7 (b) 0.947 0.006 162.064 0.000 0.892 0.892 trf91e7 (c) 0.989 0.006 178.433 0.000 0.933 0.933 trf94e7 (d) 0.925 0.007 136.237 0.000 0.872 0.872 trf95e7 (e) 0.838 0.011 73.314 0.000 0.790 0.790 trf96e7 (f) 0.932 0.007 134.099 0.000 0.878 0.878 trf97e7 (g) 0.943 0.006 148.395 0.000 0.889 0.889 trf98e7 (h) 0.825 0.012 66.459 0.000 0.778 0.778 trf99e7 (i) 0.894 0.009 103.014 0.000 0.843 0.843 Finat10 =~
trf89e10 (a) 1.000 0.947 0.947 trf90e10 (b) 0.947 0.006 162.064 0.000 0.896 0.896 trf91e10 (c) 0.989 0.006 178.433 0.000 0.937 0.937 trf94e10 (d) 0.925 0.007 136.237 0.000 0.876 0.876 trf95e10 (e) 0.838 0.011 73.314 0.000 0.793 0.793 trf96e10 (f) 0.932 0.007 134.099 0.000 0.882 0.882 trf97e10 (g) 0.943 0.006 148.395 0.000 0.893 0.893 trf98e10 (h) 0.825 0.012 66.459 0.000 0.781 0.781 trf99e10 (i) 0.894 0.009 103.014 0.000 0.846 0.846 Finat12 =~
trf89e12 (a) 1.000 0.960 0.960 trf90e12 (b) 0.947 0.006 162.064 0.000 0.908 0.908 trf91e12 (c) 0.989 0.006 178.433 0.000 0.950 0.950 trf94e12 (d) 0.925 0.007 136.237 0.000 0.888 0.888 trf95e12 (e) 0.838 0.011 73.314 0.000 0.804 0.804 trf96e12 (f) 0.932 0.007 134.099 0.000 0.894 0.894 trf97e12 (g) 0.943 0.006 148.395 0.000 0.905 0.905 trf98e12 (h) 0.825 0.012 66.459 0.000 0.792 0.792 trf99e12 (i) 0.894 0.009 103.014 0.000 0.858 0.858 Fsi5 =~
trf11e5 (j) 1.000 0.679 0.679 trf19e5 (k) 1.330 0.051 26.327 0.000 0.903 0.903 trf24e5 (l) 1.082 0.042 25.967 0.000 0.735 0.735 trf30e5 (m) 0.965 0.045 21.635 0.000 0.656 0.656 trf34e5 (n) 1.311 0.047 28.097 0.000 0.890 0.890 trf77e5 (o) 0.998 0.043 23.266 0.000 0.678 0.678 Fsi7 =~
trf11e7 (j) 1.000 0.690 0.690 trf19e7 (k) 1.330 0.051 26.327 0.000 0.918 0.918 trf24e7 (l) 1.082 0.042 25.967 0.000 0.747 0.747 trf30e7 (m) 0.965 0.045 21.635 0.000 0.666 0.666 trf34e7 (n) 1.311 0.047 28.097 0.000 0.905 0.905 trf77e7 (o) 0.998 0.043 23.266 0.000 0.689 0.689 Fsi10 =~
trf11e10 (j) 1.000 0.701 0.701 trf19e10 (k) 1.330 0.051 26.327 0.000 0.933 0.933 trf24e10 (l) 1.082 0.042 25.967 0.000 0.759 0.759 trf30e10 (m) 0.965 0.045 21.635 0.000 0.677 0.677 trf34e10 (n) 1.311 0.047 28.097 0.000 0.919 0.919 trf77e10 (o) 0.998 0.043 23.266 0.000 0.700 0.700 Fsi12 =~
trf11e12 (j) 1.000 0.707 0.707 trf19e12 (k) 1.330 0.051 26.327 0.000 0.941 0.941 trf24e12 (l) 1.082 0.042 25.967 0.000 0.765 0.765 trf30e12 (m) 0.965 0.045 21.635 0.000 0.683 0.683 trf34e12 (n) 1.311 0.047 28.097 0.000 0.927 0.927 trf77e12 (o) 0.998 0.043 23.266 0.000 0.706 0.706 RIinat =~
Finat5 1.000 0.667 0.667 Finat7 1.000 0.665 0.665 Finat10 1.000 0.662 0.662 Finat12 1.000 0.653 0.653 RIsi =~
Fsi5 1.000 0.616 0.616 Fsi7 1.000 0.606 0.606 Fsi10 1.000 0.597 0.597 Fsi12 1.000 0.592 0.592 WFinat5 =~
Finat5 1.000 0.745 0.745 WFinat7 =~
Finat7 1.000 0.747 0.747 WFinat10 =~
Finat10 1.000 0.750 0.750 WFinat12 =~
Finat12 1.000 0.757 0.757 WFsi5 =~
Fsi5 1.000 0.788 0.788 WFsi7 =~
Fsi7 1.000 0.795 0.795 WFsi10 =~
Fsi10 1.000 0.802 0.802 WFsi12 =~
Fsi12 1.000 0.806 0.806

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFinat7 ~
WFinat5 0.372 0.055 6.812 0.000 0.370 0.370 WFsi5 -0.320 0.091 -3.507 0.000 -0.243 -0.243 WFsi7 ~
WFinat5 -0.105 0.047 -2.259 0.024 -0.134 -0.134 WFsi5 0.318 0.085 3.718 0.000 0.310 0.310 WFinat10 ~
WFinat7 0.371 0.064 5.806 0.000 0.369 0.369 WFsi7 -0.370 0.091 -4.062 0.000 -0.286 -0.286 WFsi10 ~
WFinat7 0.058 0.063 0.910 0.363 0.072 0.072 WFsi7 0.093 0.102 0.915 0.360 0.091 0.091 WFinat12 ~
WFinat10 0.283 0.067 4.207 0.000 0.276 0.276 WFsi10 -0.129 0.097 -1.325 0.185 -0.100 -0.100 WFsi12 ~
WFinat10 -0.046 0.059 -0.778 0.437 -0.057 -0.057 WFsi10 0.323 0.080 4.028 0.000 0.319 0.319

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .WFinat7 ~~
.WFsi7 0.158 0.020 7.927 0.000 0.461 0.461 .WFinat10 ~~
.WFsi10 0.173 0.021 8.236 0.000 0.472 0.472 .WFinat12 ~~
.WFsi12 0.185 0.019 9.486 0.000 0.483 0.483 RIinat ~~
WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIsi ~~
WFinat5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIinat ~~
RIsi 0.225 0.019 11.676 0.000 0.858 0.858 WFinat5 ~~
WFsi5 0.122 0.021 5.803 0.000 0.326 0.326

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf89e5 (p) -0.001 0.011 -0.052 0.959 -0.001 -0.001 .trf89e7 (p) -0.001 0.011 -0.052 0.959 -0.001 -0.001 .trf89e10 (p) -0.001 0.011 -0.052 0.959 -0.001 -0.001 .trf89e12 (p) -0.001 0.011 -0.052 0.959 -0.001 -0.001 .trf90e5 (q) -0.022 0.011 -2.028 0.043 -0.022 -0.022 .trf90e7 (q) -0.022 0.011 -2.028 0.043 -0.022 -0.022 .trf90e10 (q) -0.022 0.011 -2.028 0.043 -0.022 -0.022 .trf90e12 (q) -0.022 0.011 -2.028 0.043 -0.022 -0.022 .trf91e5 (r) -0.002 0.010 -0.164 0.870 -0.002 -0.002 .trf91e7 (r) -0.002 0.010 -0.164 0.870 -0.002 -0.002 .trf91e10 (r) -0.002 0.010 -0.164 0.870 -0.002 -0.002 .trf91e12 (r) -0.002 0.010 -0.164 0.870 -0.002 -0.002 .trf94e5 (s) 0.043 0.010 4.202 0.000 0.043 0.043 .trf94e7 (s) 0.043 0.010 4.202 0.000 0.043 0.043 .trf94e10 (s) 0.043 0.010 4.202 0.000 0.043 0.043 .trf94e12 (s) 0.043 0.010 4.202 0.000 0.043 0.043 .trf95e5 (t) -0.023 0.015 -1.522 0.128 -0.023 -0.023 .trf95e7 (t) -0.023 0.015 -1.522 0.128 -0.023 -0.023 .trf95e10 (t) -0.023 0.015 -1.522 0.128 -0.023 -0.023 .trf95e12 (t) -0.023 0.015 -1.522 0.128 -0.023 -0.023 .trf96e5 (u) -0.011 0.011 -1.048 0.294 -0.011 -0.011 .trf96e7 (u) -0.011 0.011 -1.048 0.294 -0.011 -0.011 .trf96e10 (u) -0.011 0.011 -1.048 0.294 -0.011 -0.011 .trf96e12 (u) -0.011 0.011 -1.048 0.294 -0.011 -0.011 .trf97e5 (v) -0.026 0.011 -2.487 0.013 -0.026 -0.026 .trf97e7 (v) -0.026 0.011 -2.487 0.013 -0.026 -0.026 .trf97e10 (v) -0.026 0.011 -2.487 0.013 -0.026 -0.026 .trf97e12 (v) -0.026 0.011 -2.487 0.013 -0.026 -0.026 .trf98e5 (w) -0.002 0.015 -0.106 0.916 -0.002 -0.002 .trf98e7 (w) -0.002 0.015 -0.106 0.916 -0.002 -0.002 .trf98e10 (w) -0.002 0.015 -0.106 0.916 -0.002 -0.002 .trf98e12 (w) -0.002 0.015 -0.106 0.916 -0.002 -0.002 .trf99e5 (x) -0.002 0.013 -0.122 0.903 -0.002 -0.002 .trf99e7 (x) -0.002 0.013 -0.122 0.903 -0.002 -0.002 .trf99e10 (x) -0.002 0.013 -0.122 0.903 -0.002 -0.002 .trf99e12 (x) -0.002 0.013 -0.122 0.903 -0.002 -0.002 .trf11e5 (y) 0.089 0.019 4.706 0.000 0.089 0.089 .trf11e7 (y) 0.089 0.019 4.706 0.000 0.089 0.089 .trf11e10 (y) 0.089 0.019 4.706 0.000 0.089 0.089 .trf11e12 (y) 0.089 0.019 4.706 0.000 0.089 0.089 .trf19e5 (z) 0.052 0.016 3.371 0.001 0.052 0.052 .trf19e7 (z) 0.052 0.016 3.371 0.001 0.052 0.052 .trf19e10 (z) 0.052 0.016 3.371 0.001 0.052 0.052 .trf19e12 (z) 0.052 0.016 3.371 0.001 0.052 0.052 .trf24e5 (aa) 0.052 0.023 2.314 0.021 0.052 0.052 .trf24e7 (aa) 0.052 0.023 2.314 0.021 0.052 0.052 .trf24e10 (aa) 0.052 0.023 2.314 0.021 0.052 0.052 .trf24e12 (aa) 0.052 0.023 2.314 0.021 0.052 0.052 .trf30e5 (ab) 0.056 0.018 3.073 0.002 0.056 0.056 .trf30e7 (ab) 0.056 0.018 3.073 0.002 0.056 0.056 .trf30e10 (ab) 0.056 0.018 3.073 0.002 0.056 0.056 .trf30e12 (ab) 0.056 0.018 3.073 0.002 0.056 0.056 .trf34e5 (ac) 0.015 0.023 0.659 0.510 0.015 0.015 .trf34e7 (ac) 0.015 0.023 0.659 0.510 0.015 0.015 .trf34e10 (ac) 0.015 0.023 0.659 0.510 0.015 0.015 .trf34e12 (ac) 0.015 0.023 0.659 0.510 0.015 0.015 .trf77e5 (ad) 0.012 0.017 0.748 0.455 0.012 0.012 .trf77e7 (ad) 0.012 0.017 0.748 0.455 0.012 0.012 .trf77e10 (ad) 0.012 0.017 0.748 0.455 0.012 0.012 .trf77e12 (ad) 0.012 0.017 0.748 0.455 0.012 0.012 .Finat7 0.004 0.023 0.192 0.848 0.005 0.005 .Finat10 0.004 0.026 0.166 0.868 0.004 0.004 .Finat12 0.005 0.029 0.163 0.871 0.005 0.005 .Fsi7 -0.035 0.030 -1.163 0.245 -0.051 -0.051 .Fsi10 -0.034 0.026 -1.312 0.190 -0.048 -0.048 .Fsi12 -0.032 0.028 -1.174 0.241 -0.046 -0.046 .Finat5 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 RIinat 0.000 0.000 0.000 RIsi 0.000 0.000 0.000 WFinat5 0.000 0.000 0.000 .WFinat7 0.000 0.000 0.000 .WFinat10 0.000 0.000 0.000 .WFinat12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf89e5|t1 0.462 0.023 20.399 0.000 0.462 0.462 trf89e5|t2 1.304 0.031 42.547 0.000 1.304 1.304 trf90e5|t1 0.522 0.024 22.166 0.000 0.522 0.522 trf90e5|t2 1.558 0.038 41.331 0.000 1.558 1.558 trf91e5|t1 0.240 0.022 10.710 0.000 0.240 0.240 trf91e5|t2 1.293 0.031 41.414 0.000 1.293 1.293 trf94e5|t1 0.428 0.024 18.180 0.000 0.428 0.428 trf94e5|t2 1.556 0.036 42.863 0.000 1.556 1.556 trf95e5|t1 0.944 0.029 33.028 0.000 0.944 0.944 trf95e5|t2 1.930 0.051 38.134 0.000 1.930 1.930 trf96e5|t1 0.555 0.025 22.618 0.000 0.555 0.555 trf96e5|t2 1.648 0.040 41.155 0.000 1.648 1.648 trf97e5|t1 0.409 0.024 17.311 0.000 0.409 0.409 trf97e5|t2 1.530 0.037 41.190 0.000 1.530 1.530 trf98e5|t1 0.974 0.028 34.425 0.000 0.974 0.974 trf98e5|t2 1.947 0.051 38.217 0.000 1.947 1.947 trf99e5|t1 0.841 0.026 31.927 0.000 0.841 0.841 trf99e5|t2 1.942 0.051 38.374 0.000 1.942 1.942 trf89e7|t1 0.610 0.022 27.299 0.000 0.610 0.610 trf89e7|t2 1.455 0.027 54.212 0.000 1.455 1.455 trf90e7|t1 0.566 0.023 24.771 0.000 0.566 0.566 trf90e7|t2 1.520 0.031 49.481 0.000 1.520 1.520 trf91e7|t1 0.309 0.023 13.688 0.000 0.309 0.309 trf91e7|t2 1.358 0.024 56.806 0.000 1.358 1.358 trf94e7|t1 0.448 0.023 19.852 0.000 0.448 0.448 trf94e7|t2 1.561 0.030 51.640 0.000 1.561 1.561 trf95e7|t1 1.092 0.027 40.323 0.000 1.092 1.092 trf95e7|t2 2.126 0.058 36.846 0.000 2.126 2.126 trf96e7|t1 0.467 0.023 20.300 0.000 0.467 0.467 trf96e7|t2 1.622 0.032 51.015 0.000 1.622 1.622 trf97e7|t1 0.503 0.023 22.267 0.000 0.503 0.503 trf97e7|t2 1.525 0.029 51.927 0.000 1.525 1.525 trf98e7|t1 1.050 0.027 38.341 0.000 1.050 1.050 trf98e7|t2 2.006 0.050 40.169 0.000 2.006 2.006 trf99e7|t1 0.792 0.025 32.325 0.000 0.792 0.792 trf99e7|t2 1.864 0.040 46.186 0.000 1.864 1.864 trf89e10|t1 0.723 0.025 29.355 0.000 0.723 0.723 trf89e10|t2 1.599 0.030 52.773 0.000 1.599 1.599 trf90e10|t1 0.653 0.025 26.373 0.000 0.653 0.653 trf90e10|t2 1.724 0.036 47.682 0.000 1.724 1.724 trf91e10|t1 0.389 0.025 15.605 0.000 0.389 0.389 trf91e10|t2 1.487 0.027 54.085 0.000 1.487 1.487 trf94e10|t1 0.455 0.025 18.382 0.000 0.455 0.455 trf94e10|t2 1.626 0.032 51.447 0.000 1.626 1.626 trf95e10|t1 1.158 0.028 41.001 0.000 1.158 1.158 trf95e10|t2 2.190 0.063 34.950 0.000 2.190 2.190 trf96e10|t1 0.628 0.024 26.718 0.000 0.628 0.628 trf96e10|t2 1.587 0.032 49.545 0.000 1.587 1.587 trf97e10|t1 0.728 0.024 30.844 0.000 0.728 0.728 trf97e10|t2 1.627 0.033 49.699 0.000 1.627 1.627 trf98e10|t1 1.198 0.028 43.079 0.000 1.198 1.198 trf98e10|t2 1.906 0.045 42.001 0.000 1.906 1.906 trf99e10|t1 1.077 0.026 41.384 0.000 1.077 1.077 trf99e10|t2 2.027 0.048 42.253 0.000 2.027 2.027 trf89e12|t1 0.776 0.026 30.284 0.000 0.776 0.776 trf89e12|t2 1.713 0.036 48.163 0.000 1.713 1.713 trf90e12|t1 0.714 0.027 26.769 0.000 0.714 0.714 trf90e12|t2 1.842 0.041 44.466 0.000 1.842 1.842 trf91e12|t1 0.451 0.027 16.897 0.000 0.451 0.451 trf91e12|t2 1.586 0.030 52.123 0.000 1.586 1.586 trf94e12|t1 0.573 0.026 21.860 0.000 0.573 0.573 trf94e12|t2 1.766 0.036 48.860 0.000 1.766 1.766 trf95e12|t1 1.053 0.030 35.246 0.000 1.053 1.053 trf95e12|t2 2.279 0.072 31.756 0.000 2.279 2.279 trf96e12|t1 0.625 0.026 24.312 0.000 0.625 0.625 trf96e12|t2 1.610 0.034 47.824 0.000 1.610 1.610 trf97e12|t1 0.816 0.025 32.126 0.000 0.816 0.816 trf97e12|t2 1.687 0.036 46.779 0.000 1.687 1.687 trf98e12|t1 1.341 0.031 43.443 0.000 1.341 1.341 trf98e12|t2 2.059 0.055 37.109 0.000 2.059 2.059 trf99e12|t1 1.135 0.028 40.232 0.000 1.135 1.135 trf99e12|t2 2.052 0.053 39.041 0.000 2.052 2.052 trf11e5|t1 1.620 0.038 42.642 0.000 1.620 1.620 trf11e5|t2 2.646 0.094 28.189 0.000 2.646 2.646 trf19e5|t1 1.193 0.030 39.510 0.000 1.193 1.193 trf19e5|t2 2.239 0.062 36.297 0.000 2.239 2.239 trf24e5|t1 1.797 0.043 42.004 0.000 1.797 1.797 trf24e5|t2 2.677 0.099 27.069 0.000 2.677 2.677 trf30e5|t1 1.176 0.031 38.512 0.000 1.176 1.176 trf30e5|t2 2.160 0.057 38.072 0.000 2.160 2.160 trf34e5|t1 1.736 0.043 40.671 0.000 1.736 1.736 trf34e5|t2 2.642 0.098 26.934 0.000 2.642 2.642 trf77e5|t1 1.071 0.029 36.368 0.000 1.071 1.071 trf77e5|t2 2.073 0.055 37.628 0.000 2.073 2.073 trf11e7|t1 1.404 0.038 36.664 0.000 1.404 1.404 trf11e7|t2 2.403 0.064 37.268 0.000 2.403 2.403 trf19e7|t1 0.975 0.039 24.887 0.000 0.975 0.975 trf19e7|t2 2.236 0.058 38.418 0.000 2.236 2.236 trf24e7|t1 1.573 0.043 36.824 0.000 1.573 1.573 trf24e7|t2 2.474 0.071 34.910 0.000 2.474 2.474 trf30e7|t1 1.282 0.038 33.797 0.000 1.282 1.282 trf30e7|t2 2.317 0.069 33.788 0.000 2.317 2.317 trf34e7|t1 1.609 0.050 32.088 0.000 1.609 1.609 trf34e7|t2 2.940 0.139 21.120 0.000 2.940 2.940 trf77e7|t1 1.160 0.035 32.677 0.000 1.160 1.160 trf77e7|t2 2.207 0.061 36.209 0.000 2.207 2.207 trf11e10|t1 1.481 0.034 43.941 0.000 1.481 1.481 trf11e10|t2 2.237 0.061 36.768 0.000 2.237 2.237 trf19e10|t1 0.881 0.032 27.653 0.000 0.881 0.881 trf19e10|t2 2.025 0.047 43.417 0.000 2.025 2.025 trf24e10|t1 1.597 0.039 40.792 0.000 1.597 1.597 trf24e10|t2 2.484 0.080 30.872 0.000 2.484 2.484 trf30e10|t1 1.330 0.034 39.295 0.000 1.330 1.330 trf30e10|t2 2.316 0.070 33.327 0.000 2.316 2.316 trf34e10|t1 1.239 0.036 34.222 0.000 1.239 1.239 trf34e10|t2 2.283 0.067 33.909 0.000 2.283 2.283 trf77e10|t1 1.187 0.031 37.787 0.000 1.187 1.187 trf77e10|t2 2.173 0.061 35.467 0.000 2.173 2.173 trf11e12|t1 1.578 0.039 40.922 0.000 1.578 1.578 trf11e12|t2 2.588 0.094 27.619 0.000 2.588 2.588 trf19e12|t1 0.901 0.034 26.286 0.000 0.901 0.901 trf19e12|t2 2.093 0.054 38.480 0.000 2.093 2.093 trf24e12|t1 1.666 0.045 36.781 0.000 1.666 1.666 trf24e12|t2 2.666 0.106 25.096 0.000 2.666 2.666 trf30e12|t1 1.122 0.034 33.141 0.000 1.122 1.122 trf30e12|t2 2.153 0.059 36.221 0.000 2.153 2.153 trf34e12|t1 1.239 0.038 32.337 0.000 1.239 1.239 trf34e12|t2 2.440 0.082 29.829 0.000 2.440 2.440 trf77e12|t1 1.020 0.033 31.180 0.000 1.020 1.020 trf77e12|t2 1.957 0.051 38.171 0.000 1.957 1.957

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Finat5 0.000 0.000 0.000 .Finat7 0.000 0.000 0.000 .Finat10 0.000 0.000 0.000 .Finat12 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 .Fsi7 0.000 0.000 0.000 .Fsi10 0.000 0.000 0.000 .Fsi12 0.000 0.000 0.000 .trf89e5 0.117 0.117 0.117 .trf90e5 0.209 0.209 0.209 .trf91e5 0.136 0.136 0.136 .trf94e5 0.244 0.244 0.244 .trf95e5 0.380 0.380 0.380 .trf96e5 0.234 0.234 0.234 .trf97e5 0.214 0.214 0.214 .trf98e5 0.399 0.399 0.399 .trf99e5 0.294 0.294 0.294 .trf89e7 0.111 0.111 0.111 .trf90e7 0.204 0.204 0.204 .trf91e7 0.130 0.130 0.130 .trf94e7 0.239 0.239 0.239 .trf95e7 0.376 0.376 0.376 .trf96e7 0.229 0.229 0.229 .trf97e7 0.209 0.209 0.209 .trf98e7 0.395 0.395 0.395 .trf99e7 0.289 0.289 0.289 .trf89e10 0.104 0.104 0.104 .trf90e10 0.197 0.197 0.197 .trf91e10 0.123 0.123 0.123 .trf94e10 0.233 0.233 0.233 .trf95e10 0.371 0.371 0.371 .trf96e10 0.222 0.222 0.222 .trf97e10 0.203 0.203 0.203 .trf98e10 0.390 0.390 0.390 .trf99e10 0.284 0.284 0.284 .trf89e12 0.079 0.079 0.079 .trf90e12 0.175 0.175 0.175 .trf91e12 0.098 0.098 0.098 .trf94e12 0.212 0.212 0.212 .trf95e12 0.353 0.353 0.353 .trf96e12 0.201 0.201 0.201 .trf97e12 0.181 0.181 0.181 .trf98e12 0.373 0.373 0.373 .trf99e12 0.264 0.264 0.264 .trf11e5 0.539 0.539 0.539 .trf19e5 0.184 0.184 0.184 .trf24e5 0.460 0.460 0.460 .trf30e5 0.570 0.570 0.570 .trf34e5 0.207 0.207 0.207 .trf77e5 0.541 0.541 0.541 .trf11e7 0.524 0.524 0.524 .trf19e7 0.157 0.157 0.157 .trf24e7 0.442 0.442 0.442 .trf30e7 0.556 0.556 0.556 .trf34e7 0.181 0.181 0.181 .trf77e7 0.526 0.526 0.526 .trf11e10 0.508 0.508 0.508 .trf19e10 0.130 0.130 0.130 .trf24e10 0.424 0.424 0.424 .trf30e10 0.542 0.542 0.542 .trf34e10 0.155 0.155 0.155 .trf77e10 0.510 0.510 0.510 .trf11e12 0.500 0.500 0.500 .trf19e12 0.115 0.115 0.115 .trf24e12 0.414 0.414 0.414 .trf30e12 0.534 0.534 0.534 .trf34e12 0.140 0.140 0.140 .trf77e12 0.502 0.502 0.502 RIinat 0.393 0.028 13.926 0.000 1.000 1.000 RIsi 0.175 0.024 7.219 0.000 1.000 1.000 WFinat5 0.490 0.028 17.497 0.000 1.000 1.000 .WFinat7 0.428 0.023 18.906 0.000 0.863 0.863 .WFinat10 0.429 0.024 18.150 0.000 0.852 0.852 .WFinat12 0.495 0.021 23.955 0.000 0.938 0.938 WFsi5 0.286 0.028 10.288 0.000 1.000 1.000 .WFsi7 0.275 0.023 11.814 0.000 0.913 0.913 .WFsi10 0.311 0.025 12.250 0.000 0.982 0.982 .WFsi12 0.296 0.025 11.813 0.000 0.911 0.911

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf89e5 1.000 1.000 1.000 trf90e5 1.000 1.000 1.000 trf91e5 1.000 1.000 1.000 trf94e5 1.000 1.000 1.000 trf95e5 1.000 1.000 1.000 trf96e5 1.000 1.000 1.000 trf97e5 1.000 1.000 1.000 trf98e5 1.000 1.000 1.000 trf99e5 1.000 1.000 1.000 trf89e7 1.000 1.000 1.000 trf90e7 1.000 1.000 1.000 trf91e7 1.000 1.000 1.000 trf94e7 1.000 1.000 1.000 trf95e7 1.000 1.000 1.000 trf96e7 1.000 1.000 1.000 trf97e7 1.000 1.000 1.000 trf98e7 1.000 1.000 1.000 trf99e7 1.000 1.000 1.000 trf89e10 1.000 1.000 1.000 trf90e10 1.000 1.000 1.000 trf91e10 1.000 1.000 1.000 trf94e10 1.000 1.000 1.000 trf95e10 1.000 1.000 1.000 trf96e10 1.000 1.000 1.000 trf97e10 1.000 1.000 1.000 trf98e10 1.000 1.000 1.000 trf99e10 1.000 1.000 1.000 trf89e12 1.000 1.000 1.000 trf90e12 1.000 1.000 1.000 trf91e12 1.000 1.000 1.000 trf94e12 1.000 1.000 1.000 trf95e12 1.000 1.000 1.000 trf96e12 1.000 1.000 1.000 trf97e12 1.000 1.000 1.000 trf98e12 1.000 1.000 1.000 trf99e12 1.000 1.000 1.000 trf11e5 1.000 1.000 1.000 trf19e5 1.000 1.000 1.000 trf24e5 1.000 1.000 1.000 trf30e5 1.000 1.000 1.000 trf34e5 1.000 1.000 1.000 trf77e5 1.000 1.000 1.000 trf11e7 1.000 1.000 1.000 trf19e7 1.000 1.000 1.000 trf24e7 1.000 1.000 1.000 trf30e7 1.000 1.000 1.000 trf34e7 1.000 1.000 1.000 trf77e7 1.000 1.000 1.000 trf11e10 1.000 1.000 1.000 trf19e10 1.000 1.000 1.000 trf24e10 1.000 1.000 1.000 trf30e10 1.000 1.000 1.000 trf34e10 1.000 1.000 1.000 trf77e10 1.000 1.000 1.000 trf11e12 1.000 1.000 1.000 trf19e12 1.000 1.000 1.000 trf24e12 1.000 1.000 1.000 trf30e12 1.000 1.000 1.000 trf34e12 1.000 1.000 1.000 trf77e12 1.000 1.000 1.000

S4 Model fit (no robust se): (We have included here the change in CFI, TLI and RMSEA compared to the S3 model) Comparative Fit Index (CFI) 0.984 (>0.95) Change in CFI: 0.011 (decrease) - worse fit Tucker-Lewis Index (TLI) 0.983 (>0.95) Change in TLI: 0.011 (decrease) - worse fit RMSEA 0.022 (≤ 0.06) Change in RMSEA: 0.011 (increase) - worse fit 90 Percent confidence interval - lower 0.021 90 Percent confidence interval - upper 0.023
SRMR 0.070 (≤ 0.08) Change in SRMR: 0.018 (increase) - worse fit

lavTestLRT(RICLPMt_multi_inat_S3.fit, RICLPMt_multi_inat_S4.fit)

According to the chi square - significantly worse fit, p<0.000001

# summary(semTools::compareFit(RICLPMt_multi_inat_S3.fit, RICLPMt_multi_inat_S4.fit, nested = TRUE)) #† indicates the best fitting model

This is slightly tricky - I would be inclined to say that change of 0.011 in fit statistics and 0.018 in SRMR is not a substantial loss in fit. Therefore, we can conclude that S4 is good fit for teacher report inattention scores and social isolation

From Hamaker et al. (2017): need to add notes here

Exported tables for this model

# Table of model fit 
RICLPM_multi_inat_S4.fit.summary.fit <- as.data.frame(t(as.data.frame(RICLPM_multi_inat_S4.fit.summary$FIT)))[,c(6,7,19,20,27,28,29,35)]
# Table of regression coefficients and covariances 
RICLPM_multi_inat_S4.fit.summary.reg <- as.tibble(RICLPM_multi_inat_S4.fit.summary$PE[c(151:165),-c(4,5)])

Here, suddenly the cross-lagged effects go negative??? Need to think about why this could be and what is means.

Multiple indicator RI-CLPM teacher report: Hyperactivity and social isolation

RICLPMt_multi_hyp_S1: Hyperactivity/Implsivity step 1

Multiple response items RICLPMt teacher report hyperactivity ADHD symptoms and social isolation: Step 1, the configural model

RICLPMt_multi_hyp_S1 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyperactivity (teacher report)
  RIhyp1 =~ 1*trf92e5 + 1*trf92e7 + 1*trf92e10 + 1*trf92e12
  RIhyp2 =~ 1*trf93e5 + 1*trf93e7 + 1*trf93e10 + 1*trf93e12
  RIhyp3 =~ 1*trf104e5 + 1*trf104e7 + 1*trf104e10 + 1*trf104e12
  RIhyp4 =~ 1*trf105e5 + 1*trf105e7 + 1*trf105e10 + 1*trf105e12
  RIhyp5 =~ 1*trf100e5 + 1*trf100e7 + 1*trf100e10 + 1*trf100e12
  RIhyp6 =~ 1*trf101e5 + 1*trf101e7 + 1*trf101e10 + 1*trf101e12
  RIhyp7 =~ 1*trf102e5 + 1*trf102e7 + 1*trf102e10 + 1*trf102e12
  RIhyp8 =~ 1*trf103e5 + 1*trf103e7 + 1*trf103e10 + 1*trf103e12
  RIhyp9 =~ 1*trf66e5 + 1*trf66e7 + 1*trf66e10 + 1*trf66e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyperactivity symptoms at 4 waves
  WFhyp5 =~ trf92e5 + trf93e5 + trf104e5 + trf105e5 + trf100e5 + trf101e5 + trf102e5 + trf103e5 + trf66e5
  WFhyp7 =~ trf92e7 + trf93e7 + trf104e7 + trf105e7 + trf100e7 + trf101e7 + trf102e7 + trf103e7 + trf66e7
  WFhyp10 =~ trf92e10 + trf93e10 + trf104e10 + trf105e10 + trf100e10 + trf101e10 + trf102e10 + trf103e10 + trf66e10
  WFhyp12 =~ trf92e12 + trf93e12 + trf104e12 + trf105e12 + trf100e12 + trf101e12 + trf102e12 + trf103e12 + trf66e12
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ trf11e5 + trf19e5 + trf24e5 + trf30e5 + trf34e5 + trf77e5 
  WFsi7 =~ trf11e7 + trf19e7 + trf24e7 + trf30e7 + trf34e7 + trf77e7 
  WFsi10 =~ trf11e10 + trf19e10 + trf24e10 + trf30e10 + trf34e10 + trf77e10 
  WFsi12 =~ trf11e12 + trf19e12 + trf24e12 + trf30e12 + trf34e12 + trf77e12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPMt_multi_hyp_S1.fit <- cfa(RICLPMt_multi_hyp_S1, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,         
                           missing = 'pairwise' 
)

summary(RICLPMt_multi_hyp_S1.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 144 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 243

Model Test User Model: Standard Robust Test Statistic 2725.386 2399.672 Degrees of freedom 1574 1574 P-value (Chi-square) 0.000 0.000 Scaling correction factor 1.955 Shift parameter 1005.787 simple second-order correction

Model Test Baseline Model:

Test statistic 403602.379 102188.984 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.002

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.997 0.992 Tucker-Lewis Index (TLI) 0.997 0.991

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.018 0.015 90 Percent confidence interval - lower 0.017 0.014 90 Percent confidence interval - upper 0.019 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.058 0.058

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIhyp1 =~
trf92e5 1.000 0.655 0.655 trf92e7 1.000 0.655 0.655 trf92e10 1.000 0.655 0.655 trf92e12 1.000 0.655 0.655 RIhyp2 =~
trf93e5 1.000 0.658 0.658 trf93e7 1.000 0.658 0.658 trf93e10 1.000 0.658 0.658 trf93e12 1.000 0.658 0.658 RIhyp3 =~
trf104e5 1.000 0.636 0.636 trf104e7 1.000 0.636 0.636 trf104e10 1.000 0.636 0.636 trf104e12 1.000 0.636 0.636 RIhyp4 =~
trf105e5 1.000 0.636 0.636 trf105e7 1.000 0.636 0.636 trf105e10 1.000 0.636 0.636 trf105e12 1.000 0.636 0.636 RIhyp5 =~
trf100e5 1.000 0.637 0.637 trf100e7 1.000 0.637 0.637 trf100e10 1.000 0.637 0.637 trf100e12 1.000 0.637 0.637 RIhyp6 =~
trf101e5 1.000 0.650 0.650 trf101e7 1.000 0.650 0.650 trf101e10 1.000 0.650 0.650 trf101e12 1.000 0.650 0.650 RIhyp7 =~
trf102e5 1.000 0.652 0.652 trf102e7 1.000 0.652 0.652 trf102e10 1.000 0.652 0.652 trf102e12 1.000 0.652 0.652 RIhyp8 =~
trf103e5 1.000 0.520 0.520 trf103e7 1.000 0.520 0.520 trf103e10 1.000 0.520 0.520 trf103e12 1.000 0.520 0.520 RIhyp9 =~
trf66e5 1.000 0.591 0.591 trf66e7 1.000 0.591 0.591 trf66e10 1.000 0.591 0.591 trf66e12 1.000 0.591 0.591 RIsi1 =~
trf11e5 1.000 0.469 0.469 trf11e7 1.000 0.469 0.469 trf11e10 1.000 0.469 0.469 trf11e12 1.000 0.469 0.469 RIsi2 =~
trf19e5 1.000 0.507 0.507 trf19e7 1.000 0.507 0.507 trf19e10 1.000 0.507 0.507 trf19e12 1.000 0.507 0.507 RIsi3 =~
trf24e5 1.000 0.467 0.467 trf24e7 1.000 0.467 0.467 trf24e10 1.000 0.467 0.467 trf24e12 1.000 0.467 0.467 RIsi4 =~
trf30e5 1.000 0.525 0.525 trf30e7 1.000 0.525 0.525 trf30e10 1.000 0.525 0.525 trf30e12 1.000 0.525 0.525 RIsi5 =~
trf34e5 1.000 0.511 0.511 trf34e7 1.000 0.511 0.511 trf34e10 1.000 0.511 0.511 trf34e12 1.000 0.511 0.511 RIsi6 =~
trf77e5 1.000 0.534 0.534 trf77e7 1.000 0.534 0.534 trf77e10 1.000 0.534 0.534 trf77e12 1.000 0.534 0.534 WFhyp5 =~
trf92e5 1.000 0.571 0.571 trf93e5 1.093 0.045 24.218 0.000 0.625 0.625 trf104e5 0.858 0.052 16.645 0.000 0.490 0.490 trf105e5 1.094 0.051 21.339 0.000 0.625 0.625 trf100e5 1.112 0.051 21.757 0.000 0.635 0.635 trf101e5 1.179 0.052 22.515 0.000 0.674 0.674 trf102e5 1.045 0.051 20.334 0.000 0.597 0.597 trf103e5 1.125 0.069 16.256 0.000 0.642 0.642 trf66e5 0.965 0.058 16.529 0.000 0.551 0.551 WFhyp7 =~
trf92e7 1.000 0.584 0.584 trf93e7 1.111 0.044 25.023 0.000 0.649 0.649 trf104e7 1.079 0.055 19.558 0.000 0.630 0.630 trf105e7 1.126 0.049 22.894 0.000 0.657 0.657 trf100e7 1.180 0.050 23.419 0.000 0.689 0.689 trf101e7 1.116 0.048 23.198 0.000 0.651 0.651 trf102e7 1.088 0.054 20.314 0.000 0.635 0.635 trf103e7 1.127 0.070 16.202 0.000 0.658 0.658 trf66e7 1.036 0.054 19.171 0.000 0.604 0.604 WFhyp10 =~
trf92e10 1.000 0.633 0.633 trf93e10 1.044 0.040 26.422 0.000 0.661 0.661 trf104e10 0.998 0.046 21.474 0.000 0.632 0.632 trf105e10 1.051 0.042 25.164 0.000 0.665 0.665 trf100e10 1.021 0.043 24.015 0.000 0.646 0.646 trf101e10 1.051 0.040 26.452 0.000 0.665 0.665 trf102e10 1.091 0.045 24.251 0.000 0.691 0.691 trf103e10 1.034 0.060 17.143 0.000 0.654 0.654 trf66e10 0.966 0.046 21.093 0.000 0.612 0.612 WFhyp12 =~
trf92e12 1.000 0.622 0.622 trf93e12 1.054 0.039 27.106 0.000 0.656 0.656 trf104e12 1.115 0.048 23.241 0.000 0.694 0.694 trf105e12 1.059 0.042 25.458 0.000 0.658 0.658 trf100e12 1.021 0.042 24.174 0.000 0.635 0.635 trf101e12 1.061 0.041 25.604 0.000 0.660 0.660 trf102e12 1.100 0.046 23.741 0.000 0.684 0.684 trf103e12 1.103 0.058 18.903 0.000 0.686 0.686 trf66e12 1.044 0.048 21.571 0.000 0.649 0.649 WFsi5 =~
trf11e5 1.000 0.485 0.485 trf19e5 1.681 0.212 7.918 0.000 0.815 0.815 trf24e5 1.380 0.162 8.494 0.000 0.669 0.669 trf30e5 0.978 0.124 7.856 0.000 0.474 0.474 trf34e5 1.507 0.177 8.493 0.000 0.731 0.731 trf77e5 1.104 0.137 8.085 0.000 0.535 0.535 WFsi7 =~
trf11e7 1.000 0.629 0.629 trf19e7 1.076 0.096 11.235 0.000 0.677 0.677 trf24e7 1.177 0.110 10.677 0.000 0.741 0.741 trf30e7 0.782 0.098 7.970 0.000 0.492 0.492 trf34e7 1.281 0.110 11.605 0.000 0.806 0.806 trf77e7 0.863 0.095 9.038 0.000 0.543 0.543 WFsi10 =~
trf11e10 1.000 0.693 0.693 trf19e10 1.133 0.094 12.018 0.000 0.785 0.785 trf24e10 1.020 0.093 11.014 0.000 0.707 0.707 trf30e10 0.814 0.090 9.050 0.000 0.564 0.564 trf34e10 1.050 0.090 11.681 0.000 0.728 0.728 trf77e10 0.888 0.090 9.875 0.000 0.616 0.616 WFsi12 =~
trf11e12 1.000 0.694 0.694 trf19e12 1.063 0.089 11.960 0.000 0.737 0.737 trf24e12 0.977 0.093 10.465 0.000 0.678 0.678 trf30e12 0.919 0.087 10.565 0.000 0.638 0.638 trf34e12 1.105 0.090 12.308 0.000 0.766 0.766 trf77e12 0.904 0.084 10.804 0.000 0.627 0.627

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.275 0.059 4.645 0.000 0.270 0.270 WFsi5 -0.116 0.079 -1.468 0.142 -0.096 -0.096 WFsi7 ~
WFhyp5 0.049 0.071 0.687 0.492 0.044 0.044 WFsi5 0.517 0.111 4.659 0.000 0.398 0.398 WFhyp10 ~
WFhyp7 0.393 0.068 5.747 0.000 0.363 0.363 WFsi7 -0.249 0.079 -3.143 0.002 -0.247 -0.247 WFsi10 ~
WFhyp7 0.074 0.079 0.928 0.354 0.062 0.062 WFsi7 0.362 0.105 3.464 0.001 0.329 0.329 WFhyp12 ~
WFhyp10 0.312 0.063 4.924 0.000 0.318 0.318 WFsi10 -0.054 0.061 -0.874 0.382 -0.060 -0.060 WFsi12 ~
WFhyp10 -0.098 0.072 -1.349 0.177 -0.089 -0.089 WFsi10 0.456 0.084 5.435 0.000 0.455 0.455

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.089 0.020 4.553 0.000 0.321 0.321 .WFhyp7 ~~
.WFsi7 0.123 0.021 5.912 0.000 0.381 0.381 .WFhyp10 ~~
.WFsi10 0.162 0.025 6.431 0.000 0.424 0.424 .WFhyp12 ~~
.WFsi12 0.143 0.023 6.193 0.000 0.385 0.385 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.441 0.025 17.292 0.000 1.023 1.023 RIhyp3 0.328 0.024 13.748 0.000 0.788 0.788 RIhyp4 0.372 0.025 15.052 0.000 0.893 0.893 RIhyp5 0.392 0.026 15.084 0.000 0.940 0.940 RIhyp6 0.394 0.026 15.219 0.000 0.926 0.926 RIhyp7 0.388 0.028 13.969 0.000 0.910 0.910 RIhyp8 0.304 0.027 11.112 0.000 0.893 0.893 RIhyp9 0.317 0.023 13.535 0.000 0.819 0.819 RIsi1 0.150 0.028 5.406 0.000 0.490 0.490 RIsi2 0.399 0.025 16.198 0.000 1.201 1.201 RIsi3 0.250 0.028 8.801 0.000 0.817 0.817 RIsi4 0.104 0.026 4.077 0.000 0.303 0.303 RIsi5 0.392 0.026 15.015 0.000 1.171 1.171 RIsi6 0.030 0.024 1.205 0.228 0.084 0.084 RIhyp2 ~~
RIhyp3 0.402 0.024 16.783 0.000 0.962 0.962 RIhyp4 0.437 0.025 17.430 0.000 1.044 1.044 RIhyp5 0.341 0.026 13.027 0.000 0.814 0.814 RIhyp6 0.352 0.026 13.407 0.000 0.824 0.824 RIhyp7 0.341 0.028 12.029 0.000 0.796 0.796 RIhyp8 0.300 0.027 10.964 0.000 0.877 0.877 RIhyp9 0.380 0.024 15.971 0.000 0.978 0.978 RIsi1 0.122 0.027 4.449 0.000 0.396 0.396 RIsi2 0.357 0.026 13.769 0.000 1.071 1.071 RIsi3 0.225 0.028 8.115 0.000 0.733 0.733 RIsi4 0.060 0.026 2.335 0.020 0.173 0.173 RIsi5 0.372 0.027 13.635 0.000 1.108 1.108 RIsi6 -0.068 0.024 -2.828 0.005 -0.194 -0.194 RIhyp3 ~~
RIhyp4 0.445 0.024 18.655 0.000 1.102 1.102 RIhyp5 0.270 0.024 11.073 0.000 0.667 0.667 RIhyp6 0.289 0.025 11.705 0.000 0.700 0.700 RIhyp7 0.262 0.027 9.842 0.000 0.632 0.632 RIhyp8 0.295 0.026 11.186 0.000 0.891 0.891 RIhyp9 0.365 0.023 16.063 0.000 0.971 0.971 RIsi1 0.090 0.026 3.392 0.001 0.301 0.301 RIsi2 0.273 0.025 10.763 0.000 0.849 0.849 RIsi3 0.184 0.028 6.518 0.000 0.621 0.621 RIsi4 -0.017 0.025 -0.703 0.482 -0.052 -0.052 RIsi5 0.287 0.028 10.337 0.000 0.884 0.884 RIsi6 -0.161 0.025 -6.533 0.000 -0.473 -0.473 RIhyp4 ~~
RIhyp5 0.316 0.025 12.467 0.000 0.780 0.780 RIhyp6 0.333 0.025 13.140 0.000 0.805 0.805 RIhyp7 0.306 0.027 11.366 0.000 0.738 0.738 RIhyp8 0.299 0.027 10.990 0.000 0.903 0.903 RIhyp9 0.350 0.024 14.625 0.000 0.930 0.930 RIsi1 0.125 0.027 4.560 0.000 0.417 0.417 RIsi2 0.354 0.026 13.712 0.000 1.099 1.099 RIsi3 0.221 0.028 7.869 0.000 0.744 0.744 RIsi4 0.055 0.026 2.095 0.036 0.165 0.165 RIsi5 0.361 0.028 12.988 0.000 1.111 1.111 RIsi6 -0.066 0.025 -2.657 0.008 -0.193 -0.193 RIhyp5 ~~
RIhyp6 0.476 0.026 17.983 0.000 1.152 1.152 RIhyp7 0.418 0.029 14.666 0.000 1.007 1.007 RIhyp8 0.287 0.028 10.068 0.000 0.866 0.866 RIhyp9 0.258 0.024 10.767 0.000 0.686 0.686 RIsi1 0.150 0.028 5.299 0.000 0.501 0.501 RIsi2 0.341 0.025 13.393 0.000 1.058 1.058 RIsi3 0.206 0.029 7.216 0.000 0.693 0.693 RIsi4 0.120 0.025 4.721 0.000 0.360 0.360 RIsi5 0.330 0.028 11.858 0.000 1.014 1.014 RIsi6 0.060 0.025 2.404 0.016 0.177 0.177 RIhyp6 ~~
RIhyp7 0.476 0.029 16.513 0.000 1.125 1.125 RIhyp8 0.304 0.028 10.699 0.000 0.900 0.900 RIhyp9 0.267 0.024 10.995 0.000 0.695 0.695 RIsi1 0.145 0.029 5.026 0.000 0.476 0.476 RIsi2 0.352 0.026 13.712 0.000 1.070 1.070 RIsi3 0.215 0.030 7.238 0.000 0.709 0.709 RIsi4 0.117 0.026 4.565 0.000 0.342 0.342 RIsi5 0.339 0.028 12.086 0.000 1.022 1.022 RIsi6 0.059 0.025 2.374 0.018 0.171 0.171 RIhyp7 ~~
RIhyp8 0.304 0.031 9.895 0.000 0.897 0.897 RIhyp9 0.230 0.026 8.876 0.000 0.596 0.596 RIsi1 0.168 0.030 5.645 0.000 0.550 0.550 RIsi2 0.357 0.026 13.550 0.000 1.082 1.082 RIsi3 0.219 0.032 6.861 0.000 0.719 0.719 RIsi4 0.160 0.026 6.070 0.000 0.468 0.468 RIsi5 0.334 0.029 11.477 0.000 1.002 1.002 RIsi6 0.111 0.025 4.448 0.000 0.318 0.318 RIhyp8 ~~
RIhyp9 0.261 0.026 9.965 0.000 0.850 0.850 RIsi1 0.039 0.029 1.334 0.182 0.159 0.159 RIsi2 0.238 0.027 8.785 0.000 0.903 0.903 RIsi3 0.132 0.031 4.223 0.000 0.541 0.541 RIsi4 0.005 0.027 0.178 0.858 0.017 0.017 RIsi5 0.223 0.029 7.639 0.000 0.841 0.841 RIsi6 -0.105 0.026 -3.998 0.000 -0.378 -0.378 RIhyp9 ~~
RIsi1 0.054 0.026 2.109 0.035 0.195 0.195 RIsi2 0.253 0.024 10.531 0.000 0.846 0.846 RIsi3 0.153 0.027 5.750 0.000 0.555 0.555 RIsi4 -0.065 0.023 -2.863 0.004 -0.209 -0.209 RIsi5 0.249 0.026 9.490 0.000 0.826 0.826 RIsi6 -0.188 0.021 -8.898 0.000 -0.594 -0.594 RIsi1 ~~
RIsi2 0.104 0.045 2.326 0.020 0.437 0.437 RIsi3 0.289 0.051 5.671 0.000 1.317 1.317 RIsi4 0.134 0.038 3.514 0.000 0.542 0.542 RIsi5 0.106 0.049 2.147 0.032 0.442 0.442 RIsi6 0.108 0.040 2.727 0.006 0.431 0.431 RIsi2 ~~
RIsi3 0.122 0.048 2.526 0.012 0.514 0.514 RIsi4 0.100 0.038 2.603 0.009 0.376 0.376 RIsi5 0.267 0.049 5.446 0.000 1.033 1.033 RIsi6 0.056 0.039 1.446 0.148 0.208 0.208 RIsi3 ~~
RIsi4 0.057 0.041 1.393 0.164 0.231 0.231 RIsi5 0.109 0.053 2.049 0.040 0.457 0.457 RIsi6 0.015 0.041 0.378 0.705 0.062 0.062 RIsi4 ~~
RIsi5 0.107 0.042 2.559 0.010 0.398 0.398 RIsi6 0.341 0.034 9.947 0.000 1.216 1.216 RIsi5 ~~
RIsi6 0.064 0.043 1.496 0.135 0.234 0.234

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf92e5 0.000 0.000 0.000 .trf92e7 0.000 0.000 0.000 .trf92e10 0.000 0.000 0.000 .trf92e12 0.000 0.000 0.000 .trf93e5 0.000 0.000 0.000 .trf93e7 0.000 0.000 0.000 .trf93e10 0.000 0.000 0.000 .trf93e12 0.000 0.000 0.000 .trf104e5 0.000 0.000 0.000 .trf104e7 0.000 0.000 0.000 .trf104e10 0.000 0.000 0.000 .trf104e12 0.000 0.000 0.000 .trf105e5 0.000 0.000 0.000 .trf105e7 0.000 0.000 0.000 .trf105e10 0.000 0.000 0.000 .trf105e12 0.000 0.000 0.000 .trf100e5 0.000 0.000 0.000 .trf100e7 0.000 0.000 0.000 .trf100e10 0.000 0.000 0.000 .trf100e12 0.000 0.000 0.000 .trf101e5 0.000 0.000 0.000 .trf101e7 0.000 0.000 0.000 .trf101e10 0.000 0.000 0.000 .trf101e12 0.000 0.000 0.000 .trf102e5 0.000 0.000 0.000 .trf102e7 0.000 0.000 0.000 .trf102e10 0.000 0.000 0.000 .trf102e12 0.000 0.000 0.000 .trf103e5 0.000 0.000 0.000 .trf103e7 0.000 0.000 0.000 .trf103e10 0.000 0.000 0.000 .trf103e12 0.000 0.000 0.000 .trf66e5 0.000 0.000 0.000 .trf66e7 0.000 0.000 0.000 .trf66e10 0.000 0.000 0.000 .trf66e12 0.000 0.000 0.000 .trf11e5 0.000 0.000 0.000 .trf11e7 0.000 0.000 0.000 .trf11e10 0.000 0.000 0.000 .trf11e12 0.000 0.000 0.000 .trf19e5 0.000 0.000 0.000 .trf19e7 0.000 0.000 0.000 .trf19e10 0.000 0.000 0.000 .trf19e12 0.000 0.000 0.000 .trf24e5 0.000 0.000 0.000 .trf24e7 0.000 0.000 0.000 .trf24e10 0.000 0.000 0.000 .trf24e12 0.000 0.000 0.000 .trf30e5 0.000 0.000 0.000 .trf30e7 0.000 0.000 0.000 .trf30e10 0.000 0.000 0.000 .trf30e12 0.000 0.000 0.000 .trf34e5 0.000 0.000 0.000 .trf34e7 0.000 0.000 0.000 .trf34e10 0.000 0.000 0.000 .trf34e12 0.000 0.000 0.000 .trf77e5 0.000 0.000 0.000 .trf77e7 0.000 0.000 0.000 .trf77e10 0.000 0.000 0.000 .trf77e12 0.000 0.000 0.000 RIhyp1 0.000 0.000 0.000 RIhyp2 0.000 0.000 0.000 RIhyp3 0.000 0.000 0.000 RIhyp4 0.000 0.000 0.000 RIhyp5 0.000 0.000 0.000 RIhyp6 0.000 0.000 0.000 RIhyp7 0.000 0.000 0.000 RIhyp8 0.000 0.000 0.000 RIhyp9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 .WFhyp7 0.000 0.000 0.000 .WFhyp10 0.000 0.000 0.000 .WFhyp12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf92e5|t1 0.864 0.032 27.379 0.000 0.864 0.864 trf92e5|t2 1.856 0.054 34.411 0.000 1.856 1.856 trf92e7|t1 0.935 0.033 28.482 0.000 0.935 0.935 trf92e7|t2 1.877 0.056 33.721 0.000 1.877 1.877 trf92e10|t1 0.896 0.033 26.962 0.000 0.896 0.896 trf92e10|t2 1.840 0.056 33.152 0.000 1.840 1.840 trf92e12|t1 0.893 0.035 25.675 0.000 0.893 0.893 trf92e12|t2 1.820 0.057 31.780 0.000 1.820 1.820 trf93e5|t1 0.416 0.028 14.695 0.000 0.416 0.416 trf93e5|t2 1.495 0.042 35.551 0.000 1.495 1.495 trf93e7|t1 0.625 0.030 20.847 0.000 0.625 0.625 trf93e7|t2 1.571 0.045 35.014 0.000 1.571 1.571 trf93e10|t1 0.814 0.032 25.156 0.000 0.814 0.814 trf93e10|t2 1.749 0.052 33.702 0.000 1.749 1.749 trf93e12|t1 0.824 0.034 24.273 0.000 0.824 0.824 trf93e12|t2 1.700 0.052 32.423 0.000 1.700 1.700 trf104e5|t1 0.633 0.030 21.404 0.000 0.633 0.633 trf104e5|t2 1.758 0.050 35.075 0.000 1.758 1.758 trf104e7|t1 0.796 0.031 25.316 0.000 0.796 0.796 trf104e7|t2 1.724 0.050 34.623 0.000 1.724 1.724 trf104e10|t1 0.963 0.034 28.096 0.000 0.963 0.963 trf104e10|t2 1.863 0.057 32.740 0.000 1.863 1.863 trf104e12|t1 0.919 0.035 26.106 0.000 0.919 0.919 trf104e12|t2 1.954 0.064 30.619 0.000 1.954 1.954 trf105e5|t1 0.503 0.029 17.507 0.000 0.503 0.503 trf105e5|t2 1.509 0.042 35.567 0.000 1.509 1.509 trf105e7|t1 0.668 0.030 22.011 0.000 0.668 0.668 trf105e7|t2 1.643 0.047 34.902 0.000 1.643 1.643 trf105e10|t1 0.959 0.034 28.017 0.000 0.959 0.959 trf105e10|t2 1.799 0.054 33.184 0.000 1.799 1.799 trf105e12|t1 0.916 0.035 26.000 0.000 0.916 0.916 trf105e12|t2 1.847 0.059 31.439 0.000 1.847 1.847 trf100e5|t1 0.482 0.029 16.834 0.000 0.482 0.482 trf100e5|t2 1.441 0.041 35.314 0.000 1.441 1.441 trf100e7|t1 0.631 0.030 21.002 0.000 0.631 0.631 trf100e7|t2 1.461 0.042 34.806 0.000 1.461 1.461 trf100e10|t1 0.950 0.034 28.023 0.000 0.950 0.950 trf100e10|t2 1.718 0.051 33.803 0.000 1.718 1.718 trf100e12|t1 0.949 0.035 26.795 0.000 0.949 0.949 trf100e12|t2 1.894 0.061 31.281 0.000 1.894 1.894 trf101e5|t1 0.687 0.030 22.938 0.000 0.687 0.687 trf101e5|t2 1.457 0.041 35.364 0.000 1.457 1.457 trf101e7|t1 0.813 0.032 25.764 0.000 0.813 0.813 trf101e7|t2 1.487 0.043 34.877 0.000 1.487 1.487 trf101e10|t1 1.040 0.035 29.485 0.000 1.040 1.040 trf101e10|t2 1.748 0.052 33.482 0.000 1.748 1.748 trf101e12|t1 0.963 0.036 26.933 0.000 0.963 0.963 trf101e12|t2 1.873 0.060 31.303 0.000 1.873 1.873 trf102e5|t1 0.914 0.032 28.528 0.000 0.914 0.914 trf102e5|t2 1.753 0.050 35.141 0.000 1.753 1.753 trf102e7|t1 1.165 0.036 32.350 0.000 1.165 1.165 trf102e7|t2 1.863 0.055 33.831 0.000 1.863 1.863 trf102e10|t1 1.354 0.041 33.162 0.000 1.354 1.354 trf102e10|t2 2.041 0.066 31.022 0.000 2.041 2.041 trf102e12|t1 1.286 0.041 31.228 0.000 1.286 1.286 trf102e12|t2 2.049 0.069 29.601 0.000 2.049 2.049 trf103e5|t1 1.182 0.036 33.096 0.000 1.182 1.182 trf103e5|t2 1.986 0.060 33.202 0.000 1.986 1.986 trf103e7|t1 1.360 0.040 34.263 0.000 1.360 1.360 trf103e7|t2 2.078 0.066 31.567 0.000 2.078 2.078 trf103e10|t1 1.321 0.040 32.903 0.000 1.321 1.321 trf103e10|t2 2.204 0.076 28.902 0.000 2.204 2.204 trf103e12|t1 1.290 0.041 31.190 0.000 1.290 1.290 trf103e12|t2 2.153 0.076 28.288 0.000 2.153 2.153 trf66e5|t1 0.603 0.029 20.576 0.000 0.603 0.603 trf66e5|t2 1.573 0.044 35.651 0.000 1.573 1.573 trf66e7|t1 0.525 0.029 17.904 0.000 0.525 0.525 trf66e7|t2 1.547 0.044 35.029 0.000 1.547 1.547 trf66e10|t1 0.541 0.030 17.887 0.000 0.541 0.541 trf66e10|t2 1.532 0.045 34.081 0.000 1.532 1.532 trf66e12|t1 0.518 0.031 16.491 0.000 0.518 0.518 trf66e12|t2 1.480 0.045 32.558 0.000 1.480 1.480 trf11e5|t1 1.531 0.043 35.552 0.000 1.531 1.531 trf11e5|t2 2.557 0.105 24.434 0.000 2.557 2.557 trf11e7|t1 1.350 0.039 34.280 0.000 1.350 1.350 trf11e7|t2 2.350 0.085 27.639 0.000 2.350 2.350 trf11e10|t1 1.425 0.042 33.839 0.000 1.425 1.425 trf11e10|t2 2.181 0.074 29.469 0.000 2.181 2.181 trf11e12|t1 1.521 0.047 32.702 0.000 1.521 1.521 trf11e12|t2 2.532 0.111 22.893 0.000 2.532 2.532 trf19e5|t1 1.141 0.035 32.581 0.000 1.141 1.141 trf19e5|t2 2.187 0.071 30.640 0.000 2.187 2.187 trf19e7|t1 0.970 0.033 29.223 0.000 0.970 0.970 trf19e7|t2 2.231 0.076 29.522 0.000 2.231 2.231 trf19e10|t1 0.873 0.033 26.515 0.000 0.873 0.873 trf19e10|t2 2.017 0.064 31.562 0.000 2.017 2.017 trf19e12|t1 0.892 0.034 25.877 0.000 0.892 0.892 trf19e12|t2 2.084 0.070 29.579 0.000 2.084 2.084 trf24e5|t1 1.745 0.050 35.075 0.000 1.745 1.745 trf24e5|t2 2.624 0.113 23.162 0.000 2.624 2.624 trf24e7|t1 1.559 0.045 35.002 0.000 1.559 1.559 trf24e7|t2 2.460 0.096 25.730 0.000 2.460 2.460 trf24e10|t1 1.580 0.046 34.099 0.000 1.580 1.580 trf24e10|t2 2.468 0.099 24.933 0.000 2.468 2.468 trf24e12|t1 1.648 0.051 32.380 0.000 1.648 1.648 trf24e12|t2 2.649 0.128 20.769 0.000 2.649 2.649 trf30e5|t1 1.120 0.035 32.299 0.000 1.120 1.120 trf30e5|t2 2.104 0.066 31.833 0.000 2.104 2.104 trf30e7|t1 1.260 0.038 33.515 0.000 1.260 1.260 trf30e7|t2 2.295 0.080 28.532 0.000 2.295 2.295 trf30e10|t1 1.306 0.039 33.065 0.000 1.306 1.306 trf30e10|t2 2.293 0.082 27.830 0.000 2.293 2.293 trf30e12|t1 1.098 0.038 29.149 0.000 1.098 1.098 trf30e12|t2 2.128 0.074 28.736 0.000 2.128 2.128 trf34e5|t1 1.721 0.049 35.327 0.000 1.721 1.721 trf34e5|t2 2.627 0.113 23.206 0.000 2.627 2.627 trf34e7|t1 1.640 0.047 34.994 0.000 1.640 1.640 trf34e7|t2 2.971 0.177 16.764 0.000 2.971 2.971 trf34e10|t1 1.268 0.039 32.750 0.000 1.268 1.268 trf34e10|t2 2.312 0.084 27.556 0.000 2.312 2.312 trf34e12|t1 1.267 0.040 31.336 0.000 1.267 1.267 trf34e12|t2 2.467 0.103 23.948 0.000 2.467 2.467 trf77e5|t1 1.058 0.034 31.255 0.000 1.058 1.058 trf77e5|t2 2.060 0.064 32.328 0.000 2.060 2.060 trf77e7|t1 1.183 0.036 32.585 0.000 1.183 1.183 trf77e7|t2 2.230 0.076 29.496 0.000 2.230 2.230 trf77e10|t1 1.208 0.038 32.018 0.000 1.208 1.208 trf77e10|t2 2.194 0.075 29.225 0.000 2.194 2.194 trf77e12|t1 1.040 0.037 28.350 0.000 1.040 1.040 trf77e12|t2 1.977 0.065 30.511 0.000 1.977 1.977

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf92e5 0.245 0.245 0.245 .trf92e7 0.230 0.230 0.230 .trf92e10 0.170 0.170 0.170 .trf92e12 0.184 0.184 0.184 .trf93e5 0.177 0.177 0.177 .trf93e7 0.147 0.147 0.147 .trf93e10 0.131 0.131 0.131 .trf93e12 0.137 0.137 0.137 .trf104e5 0.356 0.356 0.356 .trf104e7 0.200 0.200 0.200 .trf104e10 0.197 0.197 0.197 .trf104e12 0.115 0.115 0.115 .trf105e5 0.205 0.205 0.205 .trf105e7 0.164 0.164 0.164 .trf105e10 0.153 0.153 0.153 .trf105e12 0.162 0.162 0.162 .trf100e5 0.191 0.191 0.191 .trf100e7 0.120 0.120 0.120 .trf100e10 0.177 0.177 0.177 .trf100e12 0.191 0.191 0.191 .trf101e5 0.124 0.124 0.124 .trf101e7 0.154 0.154 0.154 .trf101e10 0.135 0.135 0.135 .trf101e12 0.143 0.143 0.143 .trf102e5 0.219 0.219 0.219 .trf102e7 0.171 0.171 0.171 .trf102e10 0.098 0.098 0.098 .trf102e12 0.107 0.107 0.107 .trf103e5 0.317 0.317 0.317 .trf103e7 0.297 0.297 0.297 .trf103e10 0.301 0.301 0.301 .trf103e12 0.259 0.259 0.259 .trf66e5 0.347 0.347 0.347 .trf66e7 0.285 0.285 0.285 .trf66e10 0.276 0.276 0.276 .trf66e12 0.229 0.229 0.229 .trf11e5 0.545 0.545 0.545 .trf11e7 0.384 0.384 0.384 .trf11e10 0.299 0.299 0.299 .trf11e12 0.298 0.298 0.298 .trf19e5 0.079 0.079 0.079 .trf19e7 0.284 0.284 0.284 .trf19e10 0.127 0.127 0.127 .trf19e12 0.200 0.200 0.200 .trf24e5 0.334 0.334 0.334 .trf24e7 0.233 0.233 0.233 .trf24e10 0.282 0.282 0.282 .trf24e12 0.322 0.322 0.322 .trf30e5 0.500 0.500 0.500 .trf30e7 0.482 0.482 0.482 .trf30e10 0.406 0.406 0.406 .trf30e12 0.318 0.318 0.318 .trf34e5 0.205 0.205 0.205 .trf34e7 0.090 0.090 0.090 .trf34e10 0.209 0.209 0.209 .trf34e12 0.152 0.152 0.152 .trf77e5 0.428 0.428 0.428 .trf77e7 0.420 0.420 0.420 .trf77e10 0.335 0.335 0.335 .trf77e12 0.321 0.321 0.321 RIhyp1 0.429 0.028 15.296 0.000 1.000 1.000 RIhyp2 0.433 0.028 15.735 0.000 1.000 1.000 RIhyp3 0.404 0.025 15.973 0.000 1.000 1.000 RIhyp4 0.405 0.027 14.870 0.000 1.000 1.000 RIhyp5 0.405 0.029 14.204 0.000 1.000 1.000 RIhyp6 0.422 0.028 14.852 0.000 1.000 1.000 RIhyp7 0.425 0.034 12.360 0.000 1.000 1.000 RIhyp8 0.271 0.036 7.535 0.000 1.000 1.000 RIhyp9 0.349 0.025 13.991 0.000 1.000 1.000 RIsi1 0.220 0.054 4.096 0.000 1.000 1.000 RIsi2 0.257 0.050 5.185 0.000 1.000 1.000 RIsi3 0.218 0.058 3.756 0.000 1.000 1.000 RIsi4 0.275 0.039 7.151 0.000 1.000 1.000 RIsi5 0.261 0.055 4.787 0.000 1.000 1.000 RIsi6 0.285 0.038 7.463 0.000 1.000 1.000 WFhyp5 0.326 0.032 10.299 0.000 1.000 1.000 .WFhyp7 0.318 0.028 11.554 0.000 0.935 0.935 .WFhyp10 0.348 0.031 11.308 0.000 0.869 0.869 .WFhyp12 0.351 0.027 13.187 0.000 0.909 0.909 WFsi5 0.235 0.058 4.025 0.000 1.000 1.000 .WFsi7 0.328 0.050 6.607 0.000 0.828 0.828 .WFsi10 0.420 0.060 7.014 0.000 0.874 0.874 .WFsi12 0.391 0.056 7.034 0.000 0.813 0.813

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf92e5 1.000 1.000 1.000 trf92e7 1.000 1.000 1.000 trf92e10 1.000 1.000 1.000 trf92e12 1.000 1.000 1.000 trf93e5 1.000 1.000 1.000 trf93e7 1.000 1.000 1.000 trf93e10 1.000 1.000 1.000 trf93e12 1.000 1.000 1.000 trf104e5 1.000 1.000 1.000 trf104e7 1.000 1.000 1.000 trf104e10 1.000 1.000 1.000 trf104e12 1.000 1.000 1.000 trf105e5 1.000 1.000 1.000 trf105e7 1.000 1.000 1.000 trf105e10 1.000 1.000 1.000 trf105e12 1.000 1.000 1.000 trf100e5 1.000 1.000 1.000 trf100e7 1.000 1.000 1.000 trf100e10 1.000 1.000 1.000 trf100e12 1.000 1.000 1.000 trf101e5 1.000 1.000 1.000 trf101e7 1.000 1.000 1.000 trf101e10 1.000 1.000 1.000 trf101e12 1.000 1.000 1.000 trf102e5 1.000 1.000 1.000 trf102e7 1.000 1.000 1.000 trf102e10 1.000 1.000 1.000 trf102e12 1.000 1.000 1.000 trf103e5 1.000 1.000 1.000 trf103e7 1.000 1.000 1.000 trf103e10 1.000 1.000 1.000 trf103e12 1.000 1.000 1.000 trf66e5 1.000 1.000 1.000 trf66e7 1.000 1.000 1.000 trf66e10 1.000 1.000 1.000 trf66e12 1.000 1.000 1.000 trf11e5 1.000 1.000 1.000 trf11e7 1.000 1.000 1.000 trf11e10 1.000 1.000 1.000 trf11e12 1.000 1.000 1.000 trf19e5 1.000 1.000 1.000 trf19e7 1.000 1.000 1.000 trf19e10 1.000 1.000 1.000 trf19e12 1.000 1.000 1.000 trf24e5 1.000 1.000 1.000 trf24e7 1.000 1.000 1.000 trf24e10 1.000 1.000 1.000 trf24e12 1.000 1.000 1.000 trf30e5 1.000 1.000 1.000 trf30e7 1.000 1.000 1.000 trf30e10 1.000 1.000 1.000 trf30e12 1.000 1.000 1.000 trf34e5 1.000 1.000 1.000 trf34e7 1.000 1.000 1.000 trf34e10 1.000 1.000 1.000 trf34e12 1.000 1.000 1.000 trf77e5 1.000 1.000 1.000 trf77e7 1.000 1.000 1.000 trf77e10 1.000 1.000 1.000 trf77e12 1.000 1.000 1.000

S1 Model fit: Comparative Fit Index (CFI) 0.992 (>0.95) Tucker-Lewis Index (TLI) 0.991 (>0.95)
RMSEA 0.015 (≤ 0.06)
90 Percent confidence interval - lower 0.014 90 Percent confidence interval - upper 0.017
SRMR 0.058 (≤ 0.08)

We can conclude that the model shows good fit.

RICLPMt_multi_hyp_S2: Hyperactivity step 2

Multiple response items RICLPMt mother report hyperactivity ADHD symptoms and social isolation: Step 2. If configural variance is supported, we next test for weak invariance (sometimes called metric invariance). This tests for the equivalence of the item loadings on the factors. Weak (metric) invariance means that each item contributes to the latent construct to a similar degree across groups. this is tested by constraining factor loadings (i.e., the loadings of the items on the constructs) to be equivalent in the two time points (Putnick and Bornstein, 2016). The model with constrained factor loadings (S2) is then compared to the configural invariance model (S1) to determine fit. If the overall model fit is significantly worse in the weak invariance model compared to the configural invariance model, it indicates that at least one loading is not equivalent across the groups, and weak invariance is not supported.

In our second step model, we constrain the factor loadings to be invariant over time using the labels a*, b*, c*, d* etc, in the “within” part of the model.

RICLPMt_multi_hyp_S2 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyperactivity (teacher report)
  RIhyp1 =~ 1*trf92e5 + 1*trf92e7 + 1*trf92e10 + 1*trf92e12
  RIhyp2 =~ 1*trf93e5 + 1*trf93e7 + 1*trf93e10 + 1*trf93e12
  RIhyp3 =~ 1*trf104e5 + 1*trf104e7 + 1*trf104e10 + 1*trf104e12
  RIhyp4 =~ 1*trf105e5 + 1*trf105e7 + 1*trf105e10 + 1*trf105e12
  RIhyp5 =~ 1*trf100e5 + 1*trf100e7 + 1*trf100e10 + 1*trf100e12
  RIhyp6 =~ 1*trf101e5 + 1*trf101e7 + 1*trf101e10 + 1*trf101e12
  RIhyp7 =~ 1*trf102e5 + 1*trf102e7 + 1*trf102e10 + 1*trf102e12
  RIhyp8 =~ 1*trf103e5 + 1*trf103e7 + 1*trf103e10 + 1*trf103e12
  RIhyp9 =~ 1*trf66e5 + 1*trf66e7 + 1*trf66e10 + 1*trf66e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyperactivity symptoms at 4 waves
  WFhyp5 =~ a*trf92e5 + b*trf93e5 + c*trf104e5 + d*trf105e5 + e*trf100e5 + f*trf101e5 + g*trf102e5 + h*trf103e5 + i*trf66e5
  WFhyp7 =~ a*trf92e7 + b*trf93e7 + c*trf104e7 + d*trf105e7 + e*trf100e7 + f*trf101e7 + g*trf102e7 + h*trf103e7 + i*trf66e7
  WFhyp10 =~ a*trf92e10 + b*trf93e10 + c*trf104e10 + d*trf105e10 + e*trf100e10 + f*trf101e10 + g*trf102e10 + h*trf103e10 + i*trf66e10
  WFhyp12 =~ a*trf92e12 + b*trf93e12 + c*trf104e12 + d*trf105e12 + e*trf100e12 + f*trf101e12 + g*trf102e12 + h*trf103e12 + i*trf66e12
  
  # Factor models for social isolation at 4 waves
  WFsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  WFsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  WFsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  WFsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPMt_multi_hyp_S2.fit <- cfa(RICLPMt_multi_hyp_S2, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPMt_multi_hyp_S2.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 132 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 316 Number of equality constraints 39

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 243

Model Test User Model: Standard Robust Test Statistic 3004.837 2492.633 Degrees of freedom 1613 1613 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.062 Shift parameter 1035.241 simple second-order correction

Model Test Baseline Model:

Test statistic 403602.379 102188.984 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.002

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.997 0.991 Tucker-Lewis Index (TLI) 0.996 0.990

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.020 0.016 90 Percent confidence interval - lower 0.019 0.014 90 Percent confidence interval - upper 0.021 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.060 0.060

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all RIhyp1 =~
trf92e5 1.000 0.652 0.652 trf92e7 1.000 0.652 0.652 trf92e10 1.000 0.652 0.652 trf92e12 1.000 0.652 0.652 RIhyp2 =~
trf93e5 1.000 0.655 0.655 trf93e7 1.000 0.655 0.655 trf93e10 1.000 0.655 0.655 trf93e12 1.000 0.655 0.655 RIhyp3 =~
trf104e5 1.000 0.630 0.630 trf104e7 1.000 0.630 0.630 trf104e10 1.000 0.630 0.630 trf104e12 1.000 0.630 0.630 RIhyp4 =~
trf105e5 1.000 0.633 0.633 trf105e7 1.000 0.633 0.633 trf105e10 1.000 0.633 0.633 trf105e12 1.000 0.633 0.633 RIhyp5 =~
trf100e5 1.000 0.635 0.635 trf100e7 1.000 0.635 0.635 trf100e10 1.000 0.635 0.635 trf100e12 1.000 0.635 0.635 RIhyp6 =~
trf101e5 1.000 0.647 0.647 trf101e7 1.000 0.647 0.647 trf101e10 1.000 0.647 0.647 trf101e12 1.000 0.647 0.647 RIhyp7 =~
trf102e5 1.000 0.649 0.649 trf102e7 1.000 0.649 0.649 trf102e10 1.000 0.649 0.649 trf102e12 1.000 0.649 0.649 RIhyp8 =~
trf103e5 1.000 0.516 0.516 trf103e7 1.000 0.516 0.516 trf103e10 1.000 0.516 0.516 trf103e12 1.000 0.516 0.516 RIhyp9 =~
trf66e5 1.000 0.587 0.587 trf66e7 1.000 0.587 0.587 trf66e10 1.000 0.587 0.587 trf66e12 1.000 0.587 0.587 RIsi1 =~
trf11e5 1.000 0.480 0.480 trf11e7 1.000 0.480 0.480 trf11e10 1.000 0.480 0.480 trf11e12 1.000 0.480 0.480 RIsi2 =~
trf19e5 1.000 0.533 0.533 trf19e7 1.000 0.533 0.533 trf19e10 1.000 0.533 0.533 trf19e12 1.000 0.533 0.533 RIsi3 =~
trf24e5 1.000 0.486 0.486 trf24e7 1.000 0.486 0.486 trf24e10 1.000 0.486 0.486 trf24e12 1.000 0.486 0.486 RIsi4 =~
trf30e5 1.000 0.529 0.529 trf30e7 1.000 0.529 0.529 trf30e10 1.000 0.529 0.529 trf30e12 1.000 0.529 0.529 RIsi5 =~
trf34e5 1.000 0.538 0.538 trf34e7 1.000 0.538 0.538 trf34e10 1.000 0.538 0.538 trf34e12 1.000 0.538 0.538 RIsi6 =~
trf77e5 1.000 0.542 0.542 trf77e7 1.000 0.542 0.542 trf77e10 1.000 0.542 0.542 trf77e12 1.000 0.542 0.542 WFhyp5 =~
trf92e5 (a) 1.000 0.577 0.577 trf93e5 (b) 1.077 0.031 34.254 0.000 0.621 0.621 trf104e5 (c) 1.042 0.039 26.992 0.000 0.601 0.601 trf105e5 (d) 1.086 0.035 30.979 0.000 0.626 0.626 trf100e5 (e) 1.076 0.035 30.956 0.000 0.620 0.620 trf101e5 (f) 1.089 0.035 31.488 0.000 0.628 0.628 trf102e5 (g) 1.082 0.039 27.963 0.000 0.624 0.624 trf103e5 (h) 1.093 0.044 24.883 0.000 0.630 0.630 trf66e5 (i) 1.010 0.037 27.656 0.000 0.583 0.583 WFhyp7 =~
trf92e7 (a) 1.000 0.607 0.607 trf93e7 (b) 1.077 0.031 34.254 0.000 0.653 0.653 trf104e7 (c) 1.042 0.039 26.992 0.000 0.632 0.632 trf105e7 (d) 1.086 0.035 30.979 0.000 0.659 0.659 trf100e7 (e) 1.076 0.035 30.956 0.000 0.653 0.653 trf101e7 (f) 1.089 0.035 31.488 0.000 0.661 0.661 trf102e7 (g) 1.082 0.039 27.963 0.000 0.657 0.657 trf103e7 (h) 1.093 0.044 24.883 0.000 0.663 0.663 trf66e7 (i) 1.010 0.037 27.656 0.000 0.613 0.613 WFhyp10 =~
trf92e10 (a) 1.000 0.616 0.616 trf93e10 (b) 1.077 0.031 34.254 0.000 0.664 0.664 trf104e10 (c) 1.042 0.039 26.992 0.000 0.642 0.642 trf105e10 (d) 1.086 0.035 30.979 0.000 0.669 0.669 trf100e10 (e) 1.076 0.035 30.956 0.000 0.663 0.663 trf101e10 (f) 1.089 0.035 31.488 0.000 0.671 0.671 trf102e10 (g) 1.082 0.039 27.963 0.000 0.667 0.667 trf103e10 (h) 1.093 0.044 24.883 0.000 0.674 0.674 trf66e10 (i) 1.010 0.037 27.656 0.000 0.623 0.623 WFhyp12 =~
trf92e12 (a) 1.000 0.623 0.623 trf93e12 (b) 1.077 0.031 34.254 0.000 0.671 0.671 trf104e12 (c) 1.042 0.039 26.992 0.000 0.649 0.649 trf105e12 (d) 1.086 0.035 30.979 0.000 0.677 0.677 trf100e12 (e) 1.076 0.035 30.956 0.000 0.670 0.670 trf101e12 (f) 1.089 0.035 31.488 0.000 0.679 0.679 trf102e12 (g) 1.082 0.039 27.963 0.000 0.674 0.674 trf103e12 (h) 1.093 0.044 24.883 0.000 0.681 0.681 trf66e12 (i) 1.010 0.037 27.656 0.000 0.630 0.630 WFsi5 =~
trf11e5 (j) 1.000 0.617 0.617 trf19e5 (k) 1.126 0.070 16.100 0.000 0.694 0.694 trf24e5 (l) 1.096 0.065 16.874 0.000 0.676 0.676 trf30e5 (m) 0.868 0.063 13.844 0.000 0.535 0.535 trf34e5 (n) 1.131 0.071 15.967 0.000 0.698 0.698 trf77e5 (o) 0.921 0.062 14.943 0.000 0.568 0.568 WFsi7 =~
trf11e7 (j) 1.000 0.631 0.631 trf19e7 (k) 1.126 0.070 16.100 0.000 0.710 0.710 trf24e7 (l) 1.096 0.065 16.874 0.000 0.692 0.692 trf30e7 (m) 0.868 0.063 13.844 0.000 0.548 0.548 trf34e7 (n) 1.131 0.071 15.967 0.000 0.714 0.714 trf77e7 (o) 0.921 0.062 14.943 0.000 0.581 0.581 WFsi10 =~
trf11e10 (j) 1.000 0.658 0.658 trf19e10 (k) 1.126 0.070 16.100 0.000 0.740 0.740 trf24e10 (l) 1.096 0.065 16.874 0.000 0.721 0.721 trf30e10 (m) 0.868 0.063 13.844 0.000 0.571 0.571 trf34e10 (n) 1.131 0.071 15.967 0.000 0.744 0.744 trf77e10 (o) 0.921 0.062 14.943 0.000 0.605 0.605 WFsi12 =~
trf11e12 (j) 1.000 0.662 0.662 trf19e12 (k) 1.126 0.070 16.100 0.000 0.746 0.746 trf24e12 (l) 1.096 0.065 16.874 0.000 0.726 0.726 trf30e12 (m) 0.868 0.063 13.844 0.000 0.575 0.575 trf34e12 (n) 1.131 0.071 15.967 0.000 0.749 0.749 trf77e12 (o) 0.921 0.062 14.943 0.000 0.610 0.610

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.293 0.060 4.914 0.000 0.278 0.278 WFsi5 -0.136 0.065 -2.107 0.035 -0.138 -0.138 WFsi7 ~
WFhyp5 0.025 0.069 0.358 0.721 0.023 0.023 WFsi5 0.372 0.086 4.344 0.000 0.364 0.364 WFhyp10 ~
WFhyp7 0.374 0.064 5.853 0.000 0.368 0.368 WFsi7 -0.257 0.074 -3.456 0.001 -0.263 -0.263 WFsi10 ~
WFhyp7 0.043 0.073 0.598 0.550 0.040 0.040 WFsi7 0.301 0.099 3.033 0.002 0.289 0.289 WFhyp12 ~
WFhyp10 0.345 0.064 5.381 0.000 0.341 0.341 WFsi10 -0.097 0.064 -1.518 0.129 -0.103 -0.103 WFsi12 ~
WFhyp10 -0.104 0.074 -1.403 0.161 -0.096 -0.096 WFsi10 0.425 0.078 5.457 0.000 0.422 0.422

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.097 0.022 4.424 0.000 0.272 0.272 .WFhyp7 ~~
.WFsi7 0.124 0.020 6.214 0.000 0.361 0.361 .WFhyp10 ~~
.WFsi10 0.144 0.022 6.593 0.000 0.403 0.403 .WFhyp12 ~~
.WFsi12 0.136 0.021 6.569 0.000 0.380 0.380 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.436 0.026 16.531 0.000 1.022 1.022 RIhyp3 0.322 0.025 12.925 0.000 0.783 0.783 RIhyp4 0.367 0.026 14.360 0.000 0.889 0.889 RIhyp5 0.390 0.027 14.630 0.000 0.942 0.942 RIhyp6 0.393 0.027 14.739 0.000 0.930 0.930 RIhyp7 0.385 0.029 13.461 0.000 0.911 0.911 RIhyp8 0.301 0.028 10.617 0.000 0.895 0.895 RIhyp9 0.313 0.024 12.857 0.000 0.816 0.816 RIsi1 0.163 0.028 5.842 0.000 0.519 0.519 RIsi2 0.415 0.024 17.274 0.000 1.192 1.192 RIsi3 0.262 0.028 9.295 0.000 0.828 0.828 RIsi4 0.114 0.026 4.427 0.000 0.330 0.330 RIsi5 0.407 0.025 15.975 0.000 1.160 1.160 RIsi6 0.040 0.025 1.603 0.109 0.113 0.113 RIhyp2 ~~
RIhyp3 0.390 0.025 15.446 0.000 0.946 0.946 RIhyp4 0.432 0.026 16.638 0.000 1.043 1.043 RIhyp5 0.339 0.027 12.592 0.000 0.815 0.815 RIhyp6 0.349 0.027 12.885 0.000 0.825 0.825 RIhyp7 0.337 0.029 11.513 0.000 0.794 0.794 RIhyp8 0.296 0.028 10.425 0.000 0.877 0.877 RIhyp9 0.374 0.025 15.081 0.000 0.972 0.972 RIsi1 0.136 0.027 4.942 0.000 0.431 0.431 RIsi2 0.374 0.025 14.844 0.000 1.071 1.071 RIsi3 0.240 0.027 8.730 0.000 0.753 0.753 RIsi4 0.071 0.026 2.717 0.007 0.204 0.204 RIsi5 0.389 0.027 14.664 0.000 1.105 1.105 RIsi6 -0.057 0.025 -2.308 0.021 -0.160 -0.160 RIhyp3 ~~
RIhyp4 0.437 0.025 17.385 0.000 1.095 1.095 RIhyp5 0.263 0.025 10.362 0.000 0.657 0.657 RIhyp6 0.283 0.026 10.990 0.000 0.693 0.693 RIhyp7 0.256 0.028 9.252 0.000 0.626 0.626 RIhyp8 0.286 0.028 10.368 0.000 0.880 0.880 RIhyp9 0.357 0.024 14.899 0.000 0.963 0.963 RIsi1 0.100 0.027 3.782 0.000 0.332 0.332 RIsi2 0.287 0.025 11.443 0.000 0.853 0.853 RIsi3 0.195 0.028 6.877 0.000 0.638 0.638 RIsi4 -0.009 0.025 -0.351 0.725 -0.027 -0.027 RIsi5 0.301 0.027 11.029 0.000 0.887 0.887 RIsi6 -0.152 0.025 -6.024 0.000 -0.444 -0.444 RIhyp4 ~~
RIhyp5 0.314 0.026 12.056 0.000 0.781 0.781 RIhyp6 0.330 0.026 12.651 0.000 0.805 0.805 RIhyp7 0.301 0.028 10.837 0.000 0.733 0.733 RIhyp8 0.294 0.028 10.423 0.000 0.900 0.900 RIhyp9 0.343 0.025 13.774 0.000 0.923 0.923 RIsi1 0.138 0.027 5.072 0.000 0.455 0.455 RIsi2 0.372 0.025 14.840 0.000 1.101 1.101 RIsi3 0.235 0.028 8.471 0.000 0.766 0.766 RIsi4 0.066 0.027 2.473 0.013 0.196 0.196 RIsi5 0.378 0.027 13.995 0.000 1.110 1.110 RIsi6 -0.054 0.025 -2.141 0.032 -0.157 -0.157 RIhyp5 ~~
RIhyp6 0.489 0.027 18.167 0.000 1.189 1.189 RIhyp7 0.416 0.029 14.237 0.000 1.010 1.010 RIhyp8 0.286 0.029 9.725 0.000 0.873 0.873 RIhyp9 0.254 0.025 10.234 0.000 0.681 0.681 RIsi1 0.164 0.028 5.857 0.000 0.539 0.539 RIsi2 0.359 0.025 14.508 0.000 1.061 1.061 RIsi3 0.221 0.028 7.865 0.000 0.717 0.717 RIsi4 0.132 0.026 5.136 0.000 0.392 0.392 RIsi5 0.347 0.027 12.757 0.000 1.015 1.015 RIsi6 0.073 0.025 2.879 0.004 0.211 0.211 RIhyp6 ~~
RIhyp7 0.476 0.030 16.049 0.000 1.134 1.134 RIhyp8 0.304 0.029 10.330 0.000 0.909 0.909 RIhyp9 0.263 0.025 10.456 0.000 0.692 0.692 RIsi1 0.160 0.029 5.562 0.000 0.513 0.513 RIsi2 0.371 0.025 14.881 0.000 1.075 1.075 RIsi3 0.230 0.029 7.855 0.000 0.732 0.732 RIsi4 0.128 0.026 4.974 0.000 0.375 0.375 RIsi5 0.357 0.027 13.060 0.000 1.024 1.024 RIsi6 0.072 0.025 2.853 0.004 0.206 0.206 RIhyp7 ~~
RIhyp8 0.301 0.032 9.492 0.000 0.898 0.898 RIhyp9 0.226 0.027 8.417 0.000 0.592 0.592 RIsi1 0.181 0.030 6.064 0.000 0.582 0.582 RIsi2 0.374 0.026 14.412 0.000 1.081 1.081 RIsi3 0.233 0.032 7.315 0.000 0.738 0.738 RIsi4 0.170 0.027 6.324 0.000 0.496 0.496 RIsi5 0.350 0.029 12.219 0.000 1.003 1.003 RIsi6 0.121 0.025 4.774 0.000 0.346 0.346 RIhyp8 ~~
RIhyp9 0.256 0.027 9.391 0.000 0.844 0.844 RIsi1 0.053 0.029 1.816 0.069 0.213 0.213 RIsi2 0.256 0.026 9.728 0.000 0.930 0.930 RIsi3 0.146 0.031 4.728 0.000 0.584 0.584 RIsi4 0.016 0.027 0.584 0.559 0.058 0.058 RIsi5 0.241 0.028 8.487 0.000 0.868 0.868 RIsi6 -0.093 0.027 -3.471 0.001 -0.333 -0.333 RIhyp9 ~~
RIsi1 0.066 0.026 2.586 0.010 0.235 0.235 RIsi2 0.268 0.023 11.455 0.000 0.856 0.856 RIsi3 0.166 0.026 6.268 0.000 0.581 0.581 RIsi4 -0.055 0.023 -2.393 0.017 -0.177 -0.177 RIsi5 0.264 0.025 10.376 0.000 0.836 0.836 RIsi6 -0.177 0.022 -8.166 0.000 -0.557 -0.557 RIsi1 ~~
RIsi2 0.120 0.043 2.833 0.005 0.471 0.471 RIsi3 0.289 0.050 5.812 0.000 1.240 1.240 RIsi4 0.136 0.038 3.564 0.000 0.535 0.535 RIsi5 0.126 0.047 2.683 0.007 0.487 0.487 RIsi6 0.110 0.039 2.827 0.005 0.425 0.425 RIsi2 ~~
RIsi3 0.140 0.045 3.110 0.002 0.541 0.541 RIsi4 0.112 0.037 2.999 0.003 0.395 0.395 RIsi5 0.300 0.044 6.761 0.000 1.047 1.047 RIsi6 0.071 0.037 1.902 0.057 0.246 0.246 RIsi3 ~~
RIsi4 0.059 0.040 1.482 0.138 0.230 0.230 RIsi5 0.131 0.050 2.602 0.009 0.502 0.502 RIsi6 0.020 0.040 0.507 0.612 0.076 0.076 RIsi4 ~~
RIsi5 0.122 0.040 3.017 0.003 0.428 0.428 RIsi6 0.338 0.035 9.726 0.000 1.180 1.180 RIsi5 ~~
RIsi6 0.079 0.041 1.939 0.052 0.271 0.271

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf92e5 0.242 0.242 0.242 .trf92e7 0.206 0.206 0.206 .trf92e10 0.195 0.195 0.195 .trf92e12 0.186 0.186 0.186 .trf93e5 0.186 0.186 0.186 .trf93e7 0.145 0.145 0.145 .trf93e10 0.131 0.131 0.131 .trf93e12 0.121 0.121 0.121 .trf104e5 0.242 0.242 0.242 .trf104e7 0.203 0.203 0.203 .trf104e10 0.190 0.190 0.190 .trf104e12 0.181 0.181 0.181 .trf105e5 0.208 0.208 0.208 .trf105e7 0.166 0.166 0.166 .trf105e10 0.152 0.152 0.152 .trf105e12 0.142 0.142 0.142 .trf100e5 0.212 0.212 0.212 .trf100e7 0.170 0.170 0.170 .trf100e10 0.157 0.157 0.157 .trf100e12 0.147 0.147 0.147 .trf101e5 0.187 0.187 0.187 .trf101e7 0.144 0.144 0.144 .trf101e10 0.131 0.131 0.131 .trf101e12 0.121 0.121 0.121 .trf102e5 0.190 0.190 0.190 .trf102e7 0.148 0.148 0.148 .trf102e10 0.135 0.135 0.135 .trf102e12 0.125 0.125 0.125 .trf103e5 0.337 0.337 0.337 .trf103e7 0.294 0.294 0.294 .trf103e10 0.280 0.280 0.280 .trf103e12 0.270 0.270 0.270 .trf66e5 0.316 0.316 0.316 .trf66e7 0.279 0.279 0.279 .trf66e10 0.267 0.267 0.267 .trf66e12 0.259 0.259 0.259 .trf11e5 0.389 0.389 0.389 .trf11e7 0.371 0.371 0.371 .trf11e10 0.337 0.337 0.337 .trf11e12 0.331 0.331 0.331 .trf19e5 0.233 0.233 0.233 .trf19e7 0.211 0.211 0.211 .trf19e10 0.167 0.167 0.167 .trf19e12 0.159 0.159 0.159 .trf24e5 0.307 0.307 0.307 .trf24e7 0.286 0.286 0.286 .trf24e10 0.244 0.244 0.244 .trf24e12 0.237 0.237 0.237 .trf30e5 0.433 0.433 0.433 .trf30e7 0.420 0.420 0.420 .trf30e10 0.394 0.394 0.394 .trf30e12 0.389 0.389 0.389 .trf34e5 0.224 0.224 0.224 .trf34e7 0.201 0.201 0.201 .trf34e10 0.157 0.157 0.157 .trf34e12 0.149 0.149 0.149 .trf77e5 0.384 0.384 0.384 .trf77e7 0.369 0.369 0.369 .trf77e10 0.340 0.340 0.340 .trf77e12 0.335 0.335 0.335 RIhyp1 0.425 0.029 14.732 0.000 1.000 1.000 RIhyp2 0.428 0.028 15.036 0.000 1.000 1.000 RIhyp3 0.397 0.027 14.948 0.000 1.000 1.000 RIhyp4 0.400 0.028 14.269 0.000 1.000 1.000 RIhyp5 0.403 0.029 13.850 0.000 1.000 1.000 RIhyp6 0.419 0.029 14.383 0.000 1.000 1.000 RIhyp7 0.421 0.035 11.898 0.000 1.000 1.000 RIhyp8 0.266 0.037 7.226 0.000 1.000 1.000 RIhyp9 0.345 0.026 13.311 0.000 1.000 1.000 RIsi1 0.230 0.053 4.332 0.000 1.000 1.000 RIsi2 0.284 0.045 6.340 0.000 1.000 1.000 RIsi3 0.236 0.056 4.230 0.000 1.000 1.000 RIsi4 0.280 0.039 7.202 0.000 1.000 1.000 RIsi5 0.290 0.050 5.806 0.000 1.000 1.000 RIsi6 0.293 0.038 7.753 0.000 1.000 1.000 WFhyp5 0.333 0.027 12.368 0.000 1.000 1.000 .WFhyp7 0.340 0.023 14.657 0.000 0.924 0.924 .WFhyp10 0.325 0.027 11.964 0.000 0.854 0.854 .WFhyp12 0.348 0.023 14.852 0.000 0.896 0.896 WFsi5 0.380 0.051 7.422 0.000 1.000 1.000 .WFsi7 0.343 0.039 8.894 0.000 0.863 0.863 .WFsi10 0.393 0.044 8.858 0.000 0.908 0.908 .WFsi12 0.368 0.041 8.919 0.000 0.839 0.839

S2 Model fit (without robust se): (We have included here the change in CFI, TLI and RMSEA compared to the S1 model) Comparative Fit Index (CFI) 0.991 (>0.95) Change in CFI: 0.001 (decrease) - worse fit Tucker-Lewis Index (TLI) 0.990 (>0.95) Change in TLI: 0.001 (decrease) - worse fit RMSEA 0.016 (≤ 0.06) Change in RMSEA: 0.001 (increase) - worse fit 90 Percent confidence interval - lower 0.014 90 Percent confidence interval - upper 0.017
SRMR 0.060 (≤ 0.08) Change in SRMR: 0.002 (increase) - worse fit

lavTestLRT(RICLPMt_multi_hyp_S1.fit, RICLPMt_multi_hyp_S2.fit)

# summary(semTools::compareFit(RICLPMt_multi_hyp_S1.fit, RICLPMt_multi_hyp_S2.fit, nested = TRUE)) #† indicates the best fitting model

Significantly worse fit to include the restrictions, p<0.0001.

The difference in model fit is still smaller than 0.01 - so we can assume that weak invariance holds - even though there is a significant chi square test.

We will now go onto strong invariance testing.

RICLPMt_multi_hyp_S3

Multiple response items RICLPMt mother report hyperactivity ADHD symptoms and social isolation: Step 3

If full or partial weak invariance is supported, the next step is to test for strong (scalar) invariance, or equivalence of item intercepts, for weak invariant items. Strong invariance means that mean differences in the latent construct capture all mean differences in the shared variance of the items. Strong invariance is tested by constraining the item intercepts to be equivalent in the two groups - with the restarint applied in the weak invariance model retained. Any items that had unequal loadings in the weak invariance model (and were allowed to vary) should be allowed to vary in the strong invariance model because it is meaningless to test for equal item intercepts if the factor loadings of the items differs across groups (Putnick and Bornstein, 2016).

Fitting a model with constraints to ensure strong factorial invariance, with a random intercept for each indicator separately.

RICLPMt_multi_hyp_S3 <- '
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) for each item of hyperactivity (teacher report)
  RIhyp1 =~ 1*trf92e5 + 1*trf92e7 + 1*trf92e10 + 1*trf92e12
  RIhyp2 =~ 1*trf93e5 + 1*trf93e7 + 1*trf93e10 + 1*trf93e12
  RIhyp3 =~ 1*trf104e5 + 1*trf104e7 + 1*trf104e10 + 1*trf104e12
  RIhyp4 =~ 1*trf105e5 + 1*trf105e7 + 1*trf105e10 + 1*trf105e12
  RIhyp5 =~ 1*trf100e5 + 1*trf100e7 + 1*trf100e10 + 1*trf100e12
  RIhyp6 =~ 1*trf101e5 + 1*trf101e7 + 1*trf101e10 + 1*trf101e12
  RIhyp7 =~ 1*trf102e5 + 1*trf102e7 + 1*trf102e10 + 1*trf102e12
  RIhyp8 =~ 1*trf103e5 + 1*trf103e7 + 1*trf103e10 + 1*trf103e12
  RIhyp9 =~ 1*trf66e5 + 1*trf66e7 + 1*trf66e10 + 1*trf66e12
  
  # Create between factors (random intercepts) for each item of social isolation (teacher report)
  RIsi1 =~ 1*trf11e5 + 1*trf11e7 + 1*trf11e10 + 1*trf11e12 
  RIsi2 =~ 1*trf19e5 + 1*trf19e7 + 1*trf19e10 + 1*trf19e12
  RIsi3 =~ 1*trf24e5 + 1*trf24e7 + 1*trf24e10 + 1*trf24e12
  RIsi4 =~ 1*trf30e5 + 1*trf30e7 + 1*trf30e10 + 1*trf30e12
  RIsi5 =~ 1*trf34e5 + 1*trf34e7 + 1*trf34e10 + 1*trf34e12
  RIsi6 =~ 1*trf77e5 + 1*trf77e7 + 1*trf77e10 + 1*trf77e12
  
  ##################################
  # WITHIN PART: MEASUREMENT MODEL #
  ##################################
  
  # Factor models for hyperactivity symptoms at 4 waves (constrained)
  WFhyp5 =~ a*trf92e5 + b*trf93e5 + c*trf104e5 + d*trf105e5 + e*trf100e5 + f*trf101e5 + g*trf102e5 + h*trf103e5 + i*trf66e5
  WFhyp7 =~ a*trf92e7 + b*trf93e7 + c*trf104e7 + d*trf105e7 + e*trf100e7 + f*trf101e7 + g*trf102e7 + h*trf103e7 + i*trf66e7
  WFhyp10 =~ a*trf92e10 + b*trf93e10 + c*trf104e10 + d*trf105e10 + e*trf100e10 + f*trf101e10 + g*trf102e10 + h*trf103e10 + i*trf66e10
  WFhyp12 =~ a*trf92e12 + b*trf93e12 + c*trf104e12 + d*trf105e12 + e*trf100e12 + f*trf101e12 + g*trf102e12 + h*trf103e12 + i*trf66e12
  
  # Factor models for social isolation at 4 waves (constrained)
  WFsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  WFsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  WFsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  WFsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  trf92e5 + trf92e7 + trf92e10 + trf92e12 ~ p*1
  trf93e5 + trf93e7 + trf93e10 + trf93e12 ~ q*1
  trf104e5 + trf104e7 + trf104e10 + trf104e12 ~ r*1
  trf105e5 + trf105e7 + trf105e10 + trf105e12 ~ s*1
  trf100e5 + trf100e7 + trf100e10 + trf100e12 ~ t*1
  trf101e5 + trf101e7 + trf101e10 + trf101e12 ~ u*1
  trf102e5 + trf102e7 + trf102e10 + trf102e12 ~ v*1
  trf103e5 + trf103e7 + trf103e10 + trf103e12 ~ w*1
  trf66e5 + trf66e7 + trf66e10 + trf66e12 ~ x*1
  
  trf11e5 + trf11e7 + trf11e10 + trf11e12 ~ y*1
  trf19e5 + trf19e7 + trf19e10 + trf19e12 ~ z*1
  trf24e5 + trf24e7 + trf24e10 + trf24e12 ~ aa*1
  trf30e5 + trf30e7 + trf30e10 + trf30e12 ~ ab*1
  trf34e5 + trf34e7 + trf34e10 + trf34e12 ~ ac*1
  trf77e5 + trf77e7 + trf77e10 + trf77e12 ~ ad*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  WFhyp7 + WFhyp10 + WFhyp12 + WFsi7 + WFsi10 + WFsi12 ~ 1
  
  #########################
  # WITHIN PART: DYNAMICS #
  #########################
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Constrain covariance of the between factors and exogenous within factors to 0
  RIhyp1 + RIhyp2 + RIhyp3 + RIhyp4 + RIhyp5 + RIhyp6 + RIhyp7 + RIhyp8 + RIhyp9 + RIsi1 + RIsi2 + RIsi3 + RIsi4 + RIsi5 + RIsi6 ~~ 0*WFsi5 + 0*WFhyp5
'

RICLPMt_multi_hyp_S3.fit <- cfa(RICLPMt_multi_hyp_S3, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

RICLPMt_multi_hyp_S3.fit.summary <- summary(RICLPMt_multi_hyp_S3.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 135 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 382 Number of equality constraints 84

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 243

Model Test User Model: Standard Robust Test Statistic 3004.837 2465.888 Degrees of freedom 1592 1592 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.075 Shift parameter 1018.014 simple second-order correction

Model Test Baseline Model:

Test statistic 403602.379 102188.984 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.002

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.996 0.991 Tucker-Lewis Index (TLI) 0.996 0.990

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.020 0.016 90 Percent confidence interval - lower 0.019 0.014 90 Percent confidence interval - upper 0.021 0.017 P-value RMSEA <= 0.05 1.000 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.060 0.060

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.097 0.022 4.424 0.000 0.272 0.272 .WFhyp7 ~~
.WFsi7 0.124 0.020 6.214 0.000 0.361 0.361 .WFhyp10 ~~
.WFsi10 0.144 0.022 6.593 0.000 0.403 0.403 .WFhyp12 ~~
.WFsi12 0.136 0.021 6.569 0.000 0.380 0.380 RIhyp1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp7 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp8 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp9 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi1 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi2 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi3 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi4 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi5 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIsi6 ~~
WFsi5 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 RIhyp1 ~~
RIhyp2 0.436 0.026 16.531 0.000 1.022 1.022 RIhyp3 0.322 0.025 12.925 0.000 0.783 0.783 RIhyp4 0.367 0.026 14.360 0.000 0.889 0.889 RIhyp5 0.390 0.027 14.630 0.000 0.942 0.942 RIhyp6 0.393 0.027 14.739 0.000 0.930 0.930 RIhyp7 0.385 0.029 13.461 0.000 0.911 0.911 RIhyp8 0.301 0.028 10.618 0.000 0.895 0.895 RIhyp9 0.313 0.024 12.857 0.000 0.816 0.816 RIsi1 0.163 0.028 5.842 0.000 0.519 0.519 RIsi2 0.415 0.024 17.274 0.000 1.192 1.192 RIsi3 0.262 0.028 9.295 0.000 0.828 0.828 RIsi4 0.114 0.026 4.427 0.000 0.330 0.330 RIsi5 0.407 0.025 15.975 0.000 1.160 1.160 RIsi6 0.040 0.025 1.603 0.109 0.113 0.113 RIhyp2 ~~
RIhyp3 0.390 0.025 15.446 0.000 0.946 0.946 RIhyp4 0.432 0.026 16.638 0.000 1.043 1.043 RIhyp5 0.339 0.027 12.592 0.000 0.815 0.815 RIhyp6 0.349 0.027 12.886 0.000 0.825 0.825 RIhyp7 0.337 0.029 11.513 0.000 0.794 0.794 RIhyp8 0.296 0.028 10.425 0.000 0.877 0.877 RIhyp9 0.374 0.025 15.081 0.000 0.972 0.972 RIsi1 0.136 0.027 4.942 0.000 0.431 0.431 RIsi2 0.374 0.025 14.844 0.000 1.071 1.071 RIsi3 0.240 0.027 8.730 0.000 0.753 0.753 RIsi4 0.071 0.026 2.717 0.007 0.204 0.204 RIsi5 0.389 0.027 14.664 0.000 1.105 1.105 RIsi6 -0.057 0.025 -2.308 0.021 -0.160 -0.160 RIhyp3 ~~
RIhyp4 0.437 0.025 17.385 0.000 1.095 1.095 RIhyp5 0.263 0.025 10.362 0.000 0.657 0.657 RIhyp6 0.283 0.026 10.990 0.000 0.693 0.693 RIhyp7 0.256 0.028 9.252 0.000 0.626 0.626 RIhyp8 0.286 0.028 10.368 0.000 0.880 0.880 RIhyp9 0.357 0.024 14.899 0.000 0.963 0.963 RIsi1 0.100 0.027 3.782 0.000 0.332 0.332 RIsi2 0.287 0.025 11.443 0.000 0.853 0.853 RIsi3 0.195 0.028 6.877 0.000 0.638 0.638 RIsi4 -0.009 0.025 -0.351 0.725 -0.027 -0.027 RIsi5 0.301 0.027 11.029 0.000 0.887 0.887 RIsi6 -0.152 0.025 -6.024 0.000 -0.444 -0.444 RIhyp4 ~~
RIhyp5 0.314 0.026 12.056 0.000 0.781 0.781 RIhyp6 0.330 0.026 12.651 0.000 0.805 0.805 RIhyp7 0.301 0.028 10.838 0.000 0.733 0.733 RIhyp8 0.294 0.028 10.423 0.000 0.900 0.900 RIhyp9 0.343 0.025 13.774 0.000 0.923 0.923 RIsi1 0.138 0.027 5.072 0.000 0.455 0.455 RIsi2 0.372 0.025 14.840 0.000 1.101 1.101 RIsi3 0.235 0.028 8.471 0.000 0.766 0.766 RIsi4 0.066 0.027 2.473 0.013 0.196 0.196 RIsi5 0.378 0.027 13.995 0.000 1.110 1.110 RIsi6 -0.054 0.025 -2.141 0.032 -0.157 -0.157 RIhyp5 ~~
RIhyp6 0.489 0.027 18.167 0.000 1.189 1.189 RIhyp7 0.416 0.029 14.237 0.000 1.010 1.010 RIhyp8 0.286 0.029 9.725 0.000 0.873 0.873 RIhyp9 0.254 0.025 10.234 0.000 0.681 0.681 RIsi1 0.164 0.028 5.857 0.000 0.539 0.539 RIsi2 0.359 0.025 14.508 0.000 1.061 1.061 RIsi3 0.221 0.028 7.865 0.000 0.717 0.717 RIsi4 0.132 0.026 5.136 0.000 0.392 0.392 RIsi5 0.347 0.027 12.757 0.000 1.015 1.015 RIsi6 0.073 0.025 2.879 0.004 0.211 0.211 RIhyp6 ~~
RIhyp7 0.476 0.030 16.049 0.000 1.134 1.134 RIhyp8 0.304 0.029 10.331 0.000 0.909 0.909 RIhyp9 0.263 0.025 10.456 0.000 0.692 0.692 RIsi1 0.160 0.029 5.562 0.000 0.513 0.513 RIsi2 0.371 0.025 14.881 0.000 1.075 1.075 RIsi3 0.230 0.029 7.855 0.000 0.732 0.732 RIsi4 0.128 0.026 4.974 0.000 0.375 0.375 RIsi5 0.357 0.027 13.060 0.000 1.024 1.024 RIsi6 0.072 0.025 2.853 0.004 0.206 0.206 RIhyp7 ~~
RIhyp8 0.301 0.032 9.492 0.000 0.898 0.898 RIhyp9 0.226 0.027 8.417 0.000 0.592 0.592 RIsi1 0.181 0.030 6.064 0.000 0.582 0.582 RIsi2 0.374 0.026 14.412 0.000 1.081 1.081 RIsi3 0.233 0.032 7.315 0.000 0.738 0.738 RIsi4 0.170 0.027 6.324 0.000 0.496 0.496 RIsi5 0.350 0.029 12.219 0.000 1.003 1.003 RIsi6 0.121 0.025 4.774 0.000 0.346 0.346 RIhyp8 ~~
RIhyp9 0.256 0.027 9.391 0.000 0.844 0.844 RIsi1 0.053 0.029 1.816 0.069 0.213 0.213 RIsi2 0.256 0.026 9.728 0.000 0.930 0.930 RIsi3 0.146 0.031 4.728 0.000 0.584 0.584 RIsi4 0.016 0.027 0.584 0.559 0.058 0.058 RIsi5 0.241 0.028 8.486 0.000 0.868 0.868 RIsi6 -0.093 0.027 -3.471 0.001 -0.333 -0.333 RIhyp9 ~~
RIsi1 0.066 0.026 2.586 0.010 0.235 0.235 RIsi2 0.268 0.023 11.455 0.000 0.856 0.856 RIsi3 0.166 0.026 6.268 0.000 0.581 0.581 RIsi4 -0.055 0.023 -2.393 0.017 -0.177 -0.177 RIsi5 0.264 0.025 10.375 0.000 0.836 0.836 RIsi6 -0.177 0.022 -8.166 0.000 -0.557 -0.557 RIsi1 ~~
RIsi2 0.120 0.043 2.834 0.005 0.471 0.471 RIsi3 0.289 0.050 5.812 0.000 1.240 1.240 RIsi4 0.136 0.038 3.564 0.000 0.535 0.535 RIsi5 0.126 0.047 2.684 0.007 0.487 0.487 RIsi6 0.110 0.039 2.827 0.005 0.425 0.425 RIsi2 ~~
RIsi3 0.140 0.045 3.110 0.002 0.541 0.541 RIsi4 0.112 0.037 2.999 0.003 0.395 0.395 RIsi5 0.300 0.044 6.761 0.000 1.047 1.047 RIsi6 0.071 0.037 1.902 0.057 0.246 0.246 RIsi3 ~~
RIsi4 0.059 0.040 1.482 0.138 0.230 0.230 RIsi5 0.131 0.050 2.602 0.009 0.502 0.502 RIsi6 0.020 0.040 0.507 0.612 0.076 0.076 RIsi4 ~~
RIsi5 0.122 0.040 3.017 0.003 0.428 0.428 RIsi6 0.338 0.035 9.726 0.000 1.180 1.180 RIsi5 ~~
RIsi6 0.079 0.041 1.939 0.052 0.271 0.271

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf92e5 (p) -0.005 0.013 -0.436 0.663 -0.005 -0.005 .trf92e7 (p) -0.005 0.013 -0.436 0.663 -0.005 -0.005 .trf92e10 (p) -0.005 0.013 -0.436 0.663 -0.005 -0.005 .trf92e12 (p) -0.005 0.013 -0.436 0.663 -0.005 -0.005 .trf93e5 (q) -0.020 0.011 -1.802 0.072 -0.020 -0.020 .trf93e7 (q) -0.020 0.011 -1.802 0.072 -0.020 -0.020 .trf93e10 (q) -0.020 0.011 -1.802 0.072 -0.020 -0.020 .trf93e12 (q) -0.020 0.011 -1.802 0.072 -0.020 -0.020 .trf104e5 (r) 0.000 0.012 0.036 0.971 0.000 0.000 .trf104e7 (r) 0.000 0.012 0.036 0.971 0.000 0.000 .trf104e10 (r) 0.000 0.012 0.036 0.971 0.000 0.000 .trf104e12 (r) 0.000 0.012 0.036 0.971 0.000 0.000 .trf105e5 (s) 0.028 0.011 2.469 0.014 0.028 0.028 .trf105e7 (s) 0.028 0.011 2.469 0.014 0.028 0.028 .trf105e10 (s) 0.028 0.011 2.469 0.014 0.028 0.028 .trf105e12 (s) 0.028 0.011 2.469 0.014 0.028 0.028 .trf100e5 (t) -0.018 0.011 -1.618 0.106 -0.018 -0.018 .trf100e7 (t) -0.018 0.011 -1.618 0.106 -0.018 -0.018 .trf100e10 (t) -0.018 0.011 -1.618 0.106 -0.018 -0.018 .trf100e12 (t) -0.018 0.011 -1.618 0.106 -0.018 -0.018 .trf101e5 (u) -0.023 0.011 -2.030 0.042 -0.023 -0.023 .trf101e7 (u) -0.023 0.011 -2.030 0.042 -0.023 -0.023 .trf101e10 (u) -0.023 0.011 -2.030 0.042 -0.023 -0.023 .trf101e12 (u) -0.023 0.011 -2.030 0.042 -0.023 -0.023 .trf102e5 (v) -0.035 0.013 -2.654 0.008 -0.035 -0.035 .trf102e7 (v) -0.035 0.013 -2.654 0.008 -0.035 -0.035 .trf102e10 (v) -0.035 0.013 -2.654 0.008 -0.035 -0.035 .trf102e12 (v) -0.035 0.013 -2.654 0.008 -0.035 -0.035 .trf103e5 (w) -0.006 0.014 -0.421 0.674 -0.006 -0.006 .trf103e7 (w) -0.006 0.014 -0.421 0.674 -0.006 -0.006 .trf103e10 (w) -0.006 0.014 -0.421 0.674 -0.006 -0.006 .trf103e12 (w) -0.006 0.014 -0.421 0.674 -0.006 -0.006 .trf66e5 (x) -0.003 0.011 -0.292 0.771 -0.003 -0.003 .trf66e7 (x) -0.003 0.011 -0.292 0.771 -0.003 -0.003 .trf66e10 (x) -0.003 0.011 -0.292 0.771 -0.003 -0.003 .trf66e12 (x) -0.003 0.011 -0.292 0.771 -0.003 -0.003 .trf11e5 (y) 0.068 0.019 3.610 0.000 0.068 0.068 .trf11e7 (y) 0.068 0.019 3.610 0.000 0.068 0.068 .trf11e10 (y) 0.068 0.019 3.610 0.000 0.068 0.068 .trf11e12 (y) 0.068 0.019 3.610 0.000 0.068 0.068 .trf19e5 (z) 0.066 0.015 4.253 0.000 0.066 0.066 .trf19e7 (z) 0.066 0.015 4.253 0.000 0.066 0.066 .trf19e10 (z) 0.066 0.015 4.253 0.000 0.066 0.066 .trf19e12 (z) 0.066 0.015 4.253 0.000 0.066 0.066 .trf24e5 (aa) 0.059 0.022 2.622 0.009 0.059 0.059 .trf24e7 (aa) 0.059 0.022 2.622 0.009 0.059 0.059 .trf24e10 (aa) 0.059 0.022 2.622 0.009 0.059 0.059 .trf24e12 (aa) 0.059 0.022 2.622 0.009 0.059 0.059 .trf30e5 (ab) 0.081 0.018 4.478 0.000 0.081 0.081 .trf30e7 (ab) 0.081 0.018 4.478 0.000 0.081 0.081 .trf30e10 (ab) 0.081 0.018 4.478 0.000 0.081 0.081 .trf30e12 (ab) 0.081 0.018 4.478 0.000 0.081 0.081 .trf34e5 (ac) 0.029 0.023 1.264 0.206 0.029 0.029 .trf34e7 (ac) 0.029 0.023 1.264 0.206 0.029 0.029 .trf34e10 (ac) 0.029 0.023 1.264 0.206 0.029 0.029 .trf34e12 (ac) 0.029 0.023 1.264 0.206 0.029 0.029 .trf77e5 (ad) 0.015 0.016 0.933 0.351 0.015 0.015 .trf77e7 (ad) 0.015 0.016 0.933 0.351 0.015 0.015 .trf77e10 (ad) 0.015 0.016 0.933 0.351 0.015 0.015 .trf77e12 (ad) 0.015 0.016 0.933 0.351 0.015 0.015 .WFhyp7 0.006 0.021 0.297 0.766 0.010 0.010 .WFhyp10 -0.006 0.026 -0.224 0.823 -0.009 -0.009 .WFhyp12 0.001 0.026 0.022 0.983 0.001 0.001 .WFsi7 -0.044 0.033 -1.344 0.179 -0.070 -0.070 .WFsi10 -0.033 0.028 -1.158 0.247 -0.050 -0.050 .WFsi12 -0.026 0.031 -0.823 0.410 -0.039 -0.039 RIhyp1 0.000 0.000 0.000 RIhyp2 0.000 0.000 0.000 RIhyp3 0.000 0.000 0.000 RIhyp4 0.000 0.000 0.000 RIhyp5 0.000 0.000 0.000 RIhyp6 0.000 0.000 0.000 RIhyp7 0.000 0.000 0.000 RIhyp8 0.000 0.000 0.000 RIhyp9 0.000 0.000 0.000 RIsi1 0.000 0.000 0.000 RIsi2 0.000 0.000 0.000 RIsi3 0.000 0.000 0.000 RIsi4 0.000 0.000 0.000 RIsi5 0.000 0.000 0.000 RIsi6 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf92e5|t1 0.859 0.026 32.832 0.000 0.859 0.859 trf92e5|t2 1.850 0.047 39.684 0.000 1.850 1.850 trf92e7|t1 0.936 0.024 39.140 0.000 0.936 0.936 trf92e7|t2 1.878 0.042 44.881 0.000 1.878 1.878 trf92e10|t1 0.898 0.025 35.806 0.000 0.898 0.898 trf92e10|t2 1.843 0.041 44.940 0.000 1.843 1.843 trf92e12|t1 0.895 0.028 32.152 0.000 0.895 0.895 trf92e12|t2 1.823 0.040 45.928 0.000 1.823 1.823 trf93e5|t1 0.396 0.023 16.911 0.000 0.396 0.396 trf93e5|t2 1.475 0.035 41.744 0.000 1.475 1.475 trf93e7|t1 0.613 0.023 26.886 0.000 0.613 0.613 trf93e7|t2 1.559 0.030 52.538 0.000 1.559 1.559 trf93e10|t1 0.803 0.025 32.597 0.000 0.803 0.803 trf93e10|t2 1.737 0.035 49.590 0.000 1.737 1.737 trf93e12|t1 0.813 0.026 30.745 0.000 0.813 0.813 trf93e12|t2 1.689 0.036 46.952 0.000 1.689 1.689 trf104e5|t1 0.634 0.025 25.553 0.000 0.634 0.634 trf104e5|t2 1.758 0.043 40.967 0.000 1.758 1.758 trf104e7|t1 0.803 0.024 33.609 0.000 0.803 0.803 trf104e7|t2 1.732 0.036 47.800 0.000 1.732 1.732 trf104e10|t1 0.972 0.027 36.620 0.000 0.972 0.972 trf104e10|t2 1.872 0.041 45.712 0.000 1.872 1.872 trf104e12|t1 0.927 0.028 33.491 0.000 0.927 0.927 trf104e12|t2 1.963 0.046 42.281 0.000 1.963 1.963 trf105e5|t1 0.531 0.023 22.640 0.000 0.531 0.531 trf105e5|t2 1.538 0.035 43.593 0.000 1.538 1.538 trf105e7|t1 0.703 0.023 30.769 0.000 0.703 0.703 trf105e7|t2 1.678 0.032 51.754 0.000 1.678 1.678 trf105e10|t1 0.996 0.025 39.494 0.000 0.996 0.996 trf105e10|t2 1.836 0.037 49.870 0.000 1.836 1.836 trf105e12|t1 0.953 0.027 34.721 0.000 0.953 0.953 trf105e12|t2 1.884 0.041 46.332 0.000 1.884 1.884 trf100e5|t1 0.464 0.023 19.821 0.000 0.464 0.464 trf100e5|t2 1.422 0.034 41.810 0.000 1.422 1.422 trf100e7|t1 0.619 0.023 26.890 0.000 0.619 0.619 trf100e7|t2 1.450 0.025 57.103 0.000 1.450 1.450 trf100e10|t1 0.940 0.025 36.898 0.000 0.940 0.940 trf100e10|t2 1.708 0.035 49.170 0.000 1.708 1.708 trf100e12|t1 0.939 0.028 33.576 0.000 0.939 0.939 trf100e12|t2 1.884 0.043 43.683 0.000 1.884 1.884 trf101e5|t1 0.664 0.024 27.355 0.000 0.664 0.664 trf101e5|t2 1.434 0.034 42.449 0.000 1.434 1.434 trf101e7|t1 0.797 0.023 35.033 0.000 0.797 0.797 trf101e7|t2 1.471 0.026 55.612 0.000 1.471 1.471 trf101e10|t1 1.026 0.024 42.406 0.000 1.026 1.026 trf101e10|t2 1.734 0.034 50.529 0.000 1.734 1.734 trf101e12|t1 0.949 0.027 34.610 0.000 0.949 0.949 trf101e12|t2 1.858 0.040 46.195 0.000 1.858 1.858 trf102e5|t1 0.879 0.026 33.306 0.000 0.879 0.879 trf102e5|t2 1.718 0.043 40.240 0.000 1.718 1.718 trf102e7|t1 1.137 0.025 45.370 0.000 1.137 1.137 trf102e7|t2 1.835 0.041 45.262 0.000 1.835 1.835 trf102e10|t1 1.328 0.027 49.822 0.000 1.328 1.328 trf102e10|t2 2.014 0.049 41.458 0.000 2.014 2.014 trf102e12|t1 1.259 0.029 43.378 0.000 1.259 1.259 trf102e12|t2 2.023 0.049 41.399 0.000 2.023 2.023 trf103e5|t1 1.176 0.030 38.587 0.000 1.176 1.176 trf103e5|t2 1.980 0.053 37.663 0.000 1.980 1.980 trf103e7|t1 1.361 0.029 47.724 0.000 1.361 1.361 trf103e7|t2 2.079 0.052 39.786 0.000 2.079 2.079 trf103e10|t1 1.324 0.030 43.663 0.000 1.324 1.324 trf103e10|t2 2.207 0.060 36.584 0.000 2.207 2.207 trf103e12|t1 1.292 0.032 40.585 0.000 1.292 1.292 trf103e12|t2 2.156 0.056 38.343 0.000 2.156 2.156 trf66e5|t1 0.600 0.024 24.499 0.000 0.600 0.600 trf66e5|t2 1.569 0.038 41.661 0.000 1.569 1.569 trf66e7|t1 0.529 0.024 22.418 0.000 0.529 0.529 trf66e7|t2 1.550 0.033 47.327 0.000 1.550 1.550 trf66e10|t1 0.546 0.026 21.041 0.000 0.546 0.546 trf66e10|t2 1.537 0.033 47.262 0.000 1.537 1.537 trf66e12|t1 0.523 0.027 19.226 0.000 0.523 0.523 trf66e12|t2 1.485 0.032 46.034 0.000 1.485 1.485 trf11e5|t1 1.599 0.038 42.284 0.000 1.599 1.599 trf11e5|t2 2.625 0.093 28.103 0.000 2.625 2.625 trf11e7|t1 1.374 0.039 35.543 0.000 1.374 1.374 trf11e7|t2 2.374 0.063 37.525 0.000 2.374 2.374 trf11e10|t1 1.448 0.033 43.362 0.000 1.448 1.448 trf11e10|t2 2.204 0.060 36.726 0.000 2.204 2.204 trf11e12|t1 1.544 0.039 40.073 0.000 1.544 1.544 trf11e12|t2 2.554 0.093 27.546 0.000 2.554 2.554 trf19e5|t1 1.207 0.030 39.886 0.000 1.207 1.207 trf19e5|t2 2.253 0.062 36.422 0.000 2.253 2.253 trf19e7|t1 0.986 0.037 26.586 0.000 0.986 0.986 trf19e7|t2 2.247 0.059 38.199 0.000 2.247 2.247 trf19e10|t1 0.887 0.031 28.793 0.000 0.887 0.887 trf19e10|t2 2.031 0.048 42.633 0.000 2.031 2.031 trf19e12|t1 0.906 0.033 27.242 0.000 0.906 0.906 trf19e12|t2 2.098 0.056 37.795 0.000 2.098 2.098 trf24e5|t1 1.804 0.043 42.388 0.000 1.804 1.804 trf24e5|t2 2.683 0.099 27.228 0.000 2.683 2.683 trf24e7|t1 1.569 0.043 36.387 0.000 1.569 1.569 trf24e7|t2 2.470 0.069 35.680 0.000 2.470 2.470 trf24e10|t1 1.589 0.039 40.493 0.000 1.589 1.589 trf24e10|t2 2.476 0.079 31.304 0.000 2.476 2.476 trf24e12|t1 1.657 0.045 36.419 0.000 1.657 1.657 trf24e12|t2 2.657 0.105 25.405 0.000 2.657 2.657 trf30e5|t1 1.200 0.030 39.394 0.000 1.200 1.200 trf30e5|t2 2.185 0.057 38.463 0.000 2.185 2.185 trf30e7|t1 1.303 0.038 34.702 0.000 1.303 1.303 trf30e7|t2 2.337 0.069 34.115 0.000 2.337 2.337 trf30e10|t1 1.347 0.034 39.914 0.000 1.347 1.347 trf30e10|t2 2.334 0.069 33.638 0.000 2.334 2.334 trf30e12|t1 1.139 0.034 33.665 0.000 1.139 1.139 trf30e12|t2 2.169 0.060 36.255 0.000 2.169 2.169 trf34e5|t1 1.751 0.043 40.849 0.000 1.751 1.751 trf34e5|t2 2.656 0.098 27.006 0.000 2.656 2.656 trf34e7|t1 1.619 0.049 33.134 0.000 1.619 1.619 trf34e7|t2 2.950 0.141 20.882 0.000 2.950 2.950 trf34e10|t1 1.245 0.036 34.359 0.000 1.245 1.245 trf34e10|t2 2.289 0.069 33.313 0.000 2.289 2.289 trf34e12|t1 1.244 0.038 32.613 0.000 1.244 1.244 trf34e12|t2 2.444 0.083 29.363 0.000 2.444 2.444 trf77e5|t1 1.074 0.029 36.517 0.000 1.074 1.074 trf77e5|t2 2.076 0.055 37.693 0.000 2.076 2.076 trf77e7|t1 1.158 0.035 32.843 0.000 1.158 1.158 trf77e7|t2 2.204 0.061 36.240 0.000 2.204 2.204 trf77e10|t1 1.181 0.031 37.647 0.000 1.181 1.181 trf77e10|t2 2.167 0.061 35.259 0.000 2.167 2.167 trf77e12|t1 1.013 0.033 30.712 0.000 1.013 1.013 trf77e12|t2 1.950 0.051 38.084 0.000 1.950 1.950

S3 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S2 model) Comparative Fit Index (CFI) 0.991 (>0.95) Change in CFI: 0.000 (increase) - same fit Tucker-Lewis Index (TLI) 0.990 (>0.95) Change in TLI: 0.000 (increase) - same fit RMSEA 0.016 (≤ 0.06) Change in RMSEA: 0.000 (decrease) - same fit 90 Percent confidence interval - lower 0.014 90 Percent confidence interval - upper 0.017
SRMR 0.060 (≤ 0.08) Change in SRMR: 0.000 (increase) - same fit

# summary(semTools::compareFit(RICLPMt_multi_hyp_S2.fit, RICLPMt_multi_hyp_S3.fit, nested = TRUE)) #† indicates the best fitting model - hashed out to keep script running

Again this function does not run - perhaps there is something wrong with the way we are running the S3 code.

But the model shows almost no change in model fit from S2 - so we assume strong measurement invariance.

RICLPMt_multi_hyp_S4: Hyperactivity step 4 - Full model

RICLPMt_multi_hyp_S4 <- '
  
  #####################
  # MEASUREMENT MODEL #
  #####################
  
  # Factor models for hyperactivity symptoms at 4 waves (constrained)
  Fhyp5 =~ a*trf92e5 + b*trf93e5 + c*trf104e5 + d*trf105e5 + e*trf100e5 + f*trf101e5 + g*trf102e5 + h*trf103e5 + i*trf66e5
  Fhyp7 =~ a*trf92e7 + b*trf93e7 + c*trf104e7 + d*trf105e7 + e*trf100e7 + f*trf101e7 + g*trf102e7 + h*trf103e7 + i*trf66e7
  Fhyp10 =~ a*trf92e10 + b*trf93e10 + c*trf104e10 + d*trf105e10 + e*trf100e10 + f*trf101e10 + g*trf102e10 + h*trf103e10 + i*trf66e10
  Fhyp12 =~ a*trf92e12 + b*trf93e12 + c*trf104e12 + d*trf105e12 + e*trf100e12 + f*trf101e12 + g*trf102e12 + h*trf103e12 + i*trf66e12
  
  # Factor models for social isolation at 4 waves (constrained)
  Fsi5 =~ j*trf11e5 + k*trf19e5 + l*trf24e5 + m*trf30e5 + n*trf34e5 + o*trf77e5 
  Fsi7 =~ j*trf11e7 + k*trf19e7 + l*trf24e7 + m*trf30e7 + n*trf34e7 + o*trf77e7 
  Fsi10 =~ j*trf11e10 + k*trf19e10 + l*trf24e10 + m*trf30e10 + n*trf34e10 + o*trf77e10 
  Fsi12 =~ j*trf11e12 + k*trf19e12 + l*trf24e12 + m*trf30e12 + n*trf34e12 + o*trf77e12
  
  # Constrained intercepts over time (this is necessary for strong factorial invariance; without these contraints we have week factorial invariance). 
  trf92e5 + trf92e7 + trf92e10 + trf92e12 ~ p*1
  trf93e5 + trf93e7 + trf93e10 + trf93e12 ~ q*1
  trf104e5 + trf104e7 + trf104e10 + trf104e12 ~ r*1
  trf105e5 + trf105e7 + trf105e10 + trf105e12 ~ s*1
  trf100e5 + trf100e7 + trf100e10 + trf100e12 ~ t*1
  trf101e5 + trf101e7 + trf101e10 + trf101e12 ~ u*1
  trf102e5 + trf102e7 + trf102e10 + trf102e12 ~ v*1
  trf103e5 + trf103e7 + trf103e10 + trf103e12 ~ w*1
  trf66e5 + trf66e7 + trf66e10 + trf66e12 ~ x*1
  
  trf11e5 + trf11e7 + trf11e10 + trf11e12 ~ y*1
  trf19e5 + trf19e7 + trf19e10 + trf19e12 ~ z*1
  trf24e5 + trf24e7 + trf24e10 + trf24e12 ~ aa*1
  trf30e5 + trf30e7 + trf30e10 + trf30e12 ~ ab*1
  trf34e5 + trf34e7 + trf34e10 + trf34e12 ~ ac*1
  trf77e5 + trf77e7 + trf77e10 + trf77e12 ~ ad*1
  
  # Free latent means from t = 2 onward (only do this in combination with the constraints on the intercepts; without these, this would not be specified).
  Fhyp7 + Fhyp10 + Fhyp12 + Fsi7 + Fsi10 + Fsi12 ~ 1
  
  ################
  # BETWEEN PART #
  ################
  
  # Create between factors (random intercepts) 
  RIhyp =~ 1*Fhyp5 + 1*Fhyp7 + 1*Fhyp10 + 1*Fhyp12
  RIsi =~ 1*Fsi5 + 1*Fsi7 + 1*Fsi10 + 1*Fsi12
  
  # Set the residual variances of all Fhyp and Fsi variables to 0. 
  Fhyp5 ~~ 0*Fhyp5
  Fhyp7 ~~ 0*Fhyp7
  Fhyp10 ~~ 0*Fhyp10
  Fhyp12 ~~ 0*Fhyp12
  Fsi5 ~~ 0*Fsi5
  Fsi7 ~~ 0*Fsi7
  Fsi10 ~~ 0*Fsi10
  Fsi12 ~~ 0*Fsi12
  
  ###############
  # WITHIN PART #
  ###############
  
  # Create the within-part
  WFhyp5 =~ 1*Fhyp5
  WFhyp7 =~ 1*Fhyp7
  WFhyp10 =~ 1*Fhyp10
  WFhyp12 =~ 1*Fhyp12
  
  WFsi5 =~ 1*Fsi5
  WFsi7 =~ 1*Fsi7
  WFsi10 =~ 1*Fsi10
  WFsi12 =~ 1*Fsi12
  
  # Specify the lagged effects between the within-person centered latent variables
  WFhyp7 + WFsi7 ~ WFhyp5 + WFsi5
  WFhyp10 + WFsi10 ~ WFhyp7 + WFsi7
  WFhyp12 + WFsi12 ~ WFhyp10 + WFsi10
  
  # Estimate the correlations within the same wave
  WFhyp5 ~~ WFsi5
  WFhyp7 ~~ WFsi7
  WFhyp10 ~~ WFsi10 
  WFhyp12 ~~ WFsi12
  
  ##########################
  # ADDITIONAL CONSTRAINTS #
  ##########################
  
  # Set correlations between the between-factors (random intercepts) and within-factors at wave 1 (age 5) at 0
  RIhyp + RIsi ~~ 0*WFhyp5 + 0*WFsi5
'

RICLPMt_multi_hyp_S4.fit <- cfa(RICLPMt_multi_hyp_S4, 
                           data = dat, 
                           estimator = "WLSMV",
                           ordered = TRUE,
                           missing = 'pairwise'
                           )

summary(RICLPMt_multi_hyp_S4.fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-10 ended normally after 79 iterations

Estimator DWLS Optimization method NLMINB Number of model parameters 265 Number of equality constraints 84

                                              Used       Total

Number of observations 2224 2232 Number of missing patterns 243

Model Test User Model: Standard Robust Test Statistic 11080.819 6085.047 Degrees of freedom 1709 1709 P-value (Chi-square) 0.000 0.000 Scaling correction factor 2.226 Shift parameter 1106.775 simple second-order correction

Model Test Baseline Model:

Test statistic 403602.379 102188.984 Degrees of freedom 1770 1770 P-value 0.000 0.000 Scaling correction factor 4.002

User Model versus Baseline Model:

Comparative Fit Index (CFI) 0.977 0.956 Tucker-Lewis Index (TLI) 0.976 0.955

Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA

Root Mean Square Error of Approximation:

RMSEA 0.050 0.034 90 Percent confidence interval - lower 0.049 0.033 90 Percent confidence interval - upper 0.051 0.035 P-value RMSEA <= 0.05 0.731 1.000

Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA

Standardized Root Mean Square Residual:

SRMR 0.099 0.099

Parameter Estimates:

Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured

Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all Fhyp5 =~
trf92e5 (a) 1.000 0.866 0.866 trf93e5 (b) 1.040 0.009 119.825 0.000 0.901 0.901 trf104e5 (c) 0.971 0.011 90.843 0.000 0.841 0.841 trf105e5 (d) 1.032 0.009 111.234 0.000 0.893 0.893 trf100e5 (e) 1.030 0.009 118.400 0.000 0.892 0.892 trf101e5 (f) 1.081 0.009 122.269 0.000 0.936 0.936 trf102e5 (g) 1.027 0.009 110.271 0.000 0.889 0.889 trf103e5 (h) 0.882 0.014 61.954 0.000 0.764 0.764 trf66e5 (i) 0.885 0.012 71.683 0.000 0.766 0.766 Fhyp7 =~
trf92e7 (a) 1.000 0.887 0.887 trf93e7 (b) 1.040 0.009 119.825 0.000 0.923 0.923 trf104e7 (c) 0.971 0.011 90.843 0.000 0.862 0.862 trf105e7 (d) 1.032 0.009 111.234 0.000 0.916 0.916 trf100e7 (e) 1.030 0.009 118.400 0.000 0.914 0.914 trf101e7 (f) 1.081 0.009 122.269 0.000 0.960 0.960 trf102e7 (g) 1.027 0.009 110.271 0.000 0.911 0.911 trf103e7 (h) 0.882 0.014 61.954 0.000 0.783 0.783 trf66e7 (i) 0.885 0.012 71.683 0.000 0.785 0.785 Fhyp10 =~
trf92e10 (a) 1.000 0.893 0.893 trf93e10 (b) 1.040 0.009 119.825 0.000 0.929 0.929 trf104e10 (c) 0.971 0.011 90.843 0.000 0.867 0.867 trf105e10 (d) 1.032 0.009 111.234 0.000 0.921 0.921 trf100e10 (e) 1.030 0.009 118.400 0.000 0.919 0.919 trf101e10 (f) 1.081 0.009 122.269 0.000 0.965 0.965 trf102e10 (g) 1.027 0.009 110.271 0.000 0.916 0.916 trf103e10 (h) 0.882 0.014 61.954 0.000 0.787 0.787 trf66e10 (i) 0.885 0.012 71.683 0.000 0.790 0.790 Fhyp12 =~
trf92e12 (a) 1.000 0.896 0.896 trf93e12 (b) 1.040 0.009 119.825 0.000 0.932 0.932 trf104e12 (c) 0.971 0.011 90.843 0.000 0.870 0.870 trf105e12 (d) 1.032 0.009 111.234 0.000 0.924 0.924 trf100e12 (e) 1.030 0.009 118.400 0.000 0.923 0.923 trf101e12 (f) 1.081 0.009 122.269 0.000 0.969 0.969 trf102e12 (g) 1.027 0.009 110.271 0.000 0.920 0.920 trf103e12 (h) 0.882 0.014 61.954 0.000 0.790 0.790 trf66e12 (i) 0.885 0.012 71.683 0.000 0.793 0.793 Fsi5 =~
trf11e5 (j) 1.000 0.661 0.661 trf19e5 (k) 1.459 0.054 27.157 0.000 0.964 0.964 trf24e5 (l) 1.172 0.044 26.628 0.000 0.775 0.775 trf30e5 (m) 0.838 0.044 19.222 0.000 0.554 0.554 trf34e5 (n) 1.417 0.051 27.984 0.000 0.936 0.936 trf77e5 (o) 0.680 0.038 18.063 0.000 0.449 0.449 Fsi7 =~
trf11e7 (j) 1.000 0.664 0.664 trf19e7 (k) 1.459 0.054 27.157 0.000 0.969 0.969 trf24e7 (l) 1.172 0.044 26.628 0.000 0.779 0.779 trf30e7 (m) 0.838 0.044 19.222 0.000 0.557 0.557 trf34e7 (n) 1.417 0.051 27.984 0.000 0.941 0.941 trf77e7 (o) 0.680 0.038 18.063 0.000 0.452 0.452 Fsi10 =~
trf11e10 (j) 1.000 0.666 0.666 trf19e10 (k) 1.459 0.054 27.157 0.000 0.972 0.972 trf24e10 (l) 1.172 0.044 26.628 0.000 0.781 0.781 trf30e10 (m) 0.838 0.044 19.222 0.000 0.558 0.558 trf34e10 (n) 1.417 0.051 27.984 0.000 0.944 0.944 trf77e10 (o) 0.680 0.038 18.063 0.000 0.453 0.453 Fsi12 =~
trf11e12 (j) 1.000 0.680 0.680 trf19e12 (k) 1.459 0.054 27.157 0.000 0.992 0.992 trf24e12 (l) 1.172 0.044 26.628 0.000 0.797 0.797 trf30e12 (m) 0.838 0.044 19.222 0.000 0.570 0.570 trf34e12 (n) 1.417 0.051 27.984 0.000 0.963 0.963 trf77e12 (o) 0.680 0.038 18.063 0.000 0.462 0.462 RIhyp =~
Fhyp5 1.000 0.686 0.686 Fhyp7 1.000 0.669 0.669 Fhyp10 1.000 0.665 0.665 Fhyp12 1.000 0.663 0.663 RIsi =~
Fsi5 1.000 0.511 0.511 Fsi7 1.000 0.508 0.508 Fsi10 1.000 0.507 0.507 Fsi12 1.000 0.496 0.496 WFhyp5 =~
Fhyp5 1.000 0.728 0.728 WFhyp7 =~
Fhyp7 1.000 0.743 0.743 WFhyp10 =~
Fhyp10 1.000 0.747 0.747 WFhyp12 =~
Fhyp12 1.000 0.749 0.749 WFsi5 =~
Fsi5 1.000 0.860 0.860 WFsi7 =~
Fsi7 1.000 0.861 0.861 WFsi10 =~
Fsi10 1.000 0.862 0.862 WFsi12 =~
Fsi12 1.000 0.868 0.868

Regressions: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp7 ~
WFhyp5 0.283 0.057 4.963 0.000 0.270 0.270 WFsi5 -0.151 0.075 -2.022 0.043 -0.130 -0.130 WFsi7 ~
WFhyp5 -0.000 0.056 -0.004 0.997 -0.000 -0.000 WFsi5 0.452 0.088 5.162 0.000 0.449 0.449 WFhyp10 ~
WFhyp7 0.379 0.061 6.268 0.000 0.375 0.375 WFsi7 -0.342 0.091 -3.771 0.000 -0.294 -0.294 WFsi10 ~
WFhyp7 0.020 0.061 0.335 0.738 0.023 0.023 WFsi7 0.352 0.112 3.136 0.002 0.351 0.351 WFhyp12 ~
WFhyp10 0.339 0.065 5.217 0.000 0.336 0.336 WFsi10 -0.112 0.084 -1.342 0.180 -0.096 -0.096 WFsi12 ~
WFhyp10 -0.115 0.062 -1.854 0.064 -0.130 -0.130 WFsi10 0.489 0.093 5.236 0.000 0.475 0.475

Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all WFhyp5 ~~
WFsi5 0.103 0.022 4.752 0.000 0.287 0.287 .WFhyp7 ~~
.WFsi7 0.131 0.018 7.508 0.000 0.404 0.404 .WFhyp10 ~~
.WFsi10 0.162 0.021 7.910 0.000 0.495 0.495 .WFhyp12 ~~
.WFsi12 0.141 0.018 7.854 0.000 0.419 0.419 RIhyp ~~
WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIsi ~~
WFhyp5 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 RIhyp ~~
RIsi 0.190 0.020 9.278 0.000 0.946 0.946

Intercepts: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .trf92e5 (p) -0.008 0.012 -0.647 0.518 -0.008 -0.008 .trf92e7 (p) -0.008 0.012 -0.647 0.518 -0.008 -0.008 .trf92e10 (p) -0.008 0.012 -0.647 0.518 -0.008 -0.008 .trf92e12 (p) -0.008 0.012 -0.647 0.518 -0.008 -0.008 .trf93e5 (q) -0.028 0.011 -2.617 0.009 -0.028 -0.028 .trf93e7 (q) -0.028 0.011 -2.617 0.009 -0.028 -0.028 .trf93e10 (q) -0.028 0.011 -2.617 0.009 -0.028 -0.028 .trf93e12 (q) -0.028 0.011 -2.617 0.009 -0.028 -0.028 .trf104e5 (r) -0.006 0.012 -0.502 0.616 -0.006 -0.006 .trf104e7 (r) -0.006 0.012 -0.502 0.616 -0.006 -0.006 .trf104e10 (r) -0.006 0.012 -0.502 0.616 -0.006 -0.006 .trf104e12 (r) -0.006 0.012 -0.502 0.616 -0.006 -0.006 .trf105e5 (s) 0.042 0.011 3.692 0.000 0.042 0.042 .trf105e7 (s) 0.042 0.011 3.692 0.000 0.042 0.042 .trf105e10 (s) 0.042 0.011 3.692 0.000 0.042 0.042 .trf105e12 (s) 0.042 0.011 3.692 0.000 0.042 0.042 .trf100e5 (t) -0.026 0.011 -2.288 0.022 -0.026 -0.026 .trf100e7 (t) -0.026 0.011 -2.288 0.022 -0.026 -0.026 .trf100e10 (t) -0.026 0.011 -2.288 0.022 -0.026 -0.026 .trf100e12 (t) -0.026 0.011 -2.288 0.022 -0.026 -0.026 .trf101e5 (u) -0.045 0.011 -4.034 0.000 -0.045 -0.045 .trf101e7 (u) -0.045 0.011 -4.034 0.000 -0.045 -0.045 .trf101e10 (u) -0.045 0.011 -4.034 0.000 -0.045 -0.045 .trf101e12 (u) -0.045 0.011 -4.034 0.000 -0.045 -0.045 .trf102e5 (v) -0.039 0.013 -2.926 0.003 -0.039 -0.039 .trf102e7 (v) -0.039 0.013 -2.926 0.003 -0.039 -0.039 .trf102e10 (v) -0.039 0.013 -2.926 0.003 -0.039 -0.039 .trf102e12 (v) -0.039 0.013 -2.926 0.003 -0.039 -0.039 .trf103e5 (w) -0.002 0.014 -0.152 0.879 -0.002 -0.002 .trf103e7 (w) -0.002 0.014 -0.152 0.879 -0.002 -0.002 .trf103e10 (w) -0.002 0.014 -0.152 0.879 -0.002 -0.002 .trf103e12 (w) -0.002 0.014 -0.152 0.879 -0.002 -0.002 .trf66e5 (x) -0.005 0.011 -0.417 0.676 -0.005 -0.005 .trf66e7 (x) -0.005 0.011 -0.417 0.676 -0.005 -0.005 .trf66e10 (x) -0.005 0.011 -0.417 0.676 -0.005 -0.005 .trf66e12 (x) -0.005 0.011 -0.417 0.676 -0.005 -0.005 .trf11e5 (y) 0.071 0.019 3.733 0.000 0.071 0.071 .trf11e7 (y) 0.071 0.019 3.733 0.000 0.071 0.071 .trf11e10 (y) 0.071 0.019 3.733 0.000 0.071 0.071 .trf11e12 (y) 0.071 0.019 3.733 0.000 0.071 0.071 .trf19e5 (z) 0.031 0.016 1.949 0.051 0.031 0.031 .trf19e7 (z) 0.031 0.016 1.949 0.051 0.031 0.031 .trf19e10 (z) 0.031 0.016 1.949 0.051 0.031 0.031 .trf19e12 (z) 0.031 0.016 1.949 0.051 0.031 0.031 .trf24e5 (aa) 0.009 0.023 0.421 0.674 0.009 0.009 .trf24e7 (aa) 0.009 0.023 0.421 0.674 0.009 0.009 .trf24e10 (aa) 0.009 0.023 0.421 0.674 0.009 0.009 .trf24e12 (aa) 0.009 0.023 0.421 0.674 0.009 0.009 .trf30e5 (ab) 0.058 0.018 3.172 0.002 0.058 0.058 .trf30e7 (ab) 0.058 0.018 3.172 0.002 0.058 0.058 .trf30e10 (ab) 0.058 0.018 3.172 0.002 0.058 0.058 .trf30e12 (ab) 0.058 0.018 3.172 0.002 0.058 0.058 .trf34e5 (ac) 0.005 0.023 0.216 0.829 0.005 0.005 .trf34e7 (ac) 0.005 0.023 0.216 0.829 0.005 0.005 .trf34e10 (ac) 0.005 0.023 0.216 0.829 0.005 0.005 .trf34e12 (ac) 0.005 0.023 0.216 0.829 0.005 0.005 .trf77e5 (ad) 0.005 0.017 0.323 0.747 0.005 0.005 .trf77e7 (ad) 0.005 0.017 0.323 0.747 0.005 0.005 .trf77e10 (ad) 0.005 0.017 0.323 0.747 0.005 0.005 .trf77e12 (ad) 0.005 0.017 0.323 0.747 0.005 0.005 .Fhyp7 0.013 0.022 0.566 0.572 0.014 0.014 .Fhyp10 0.011 0.026 0.436 0.663 0.013 0.013 .Fhyp12 0.012 0.028 0.412 0.680 0.013 0.013 .Fsi7 -0.023 0.031 -0.736 0.462 -0.034 -0.034 .Fsi10 -0.021 0.025 -0.825 0.409 -0.031 -0.031 .Fsi12 -0.021 0.028 -0.765 0.444 -0.031 -0.031 .Fhyp5 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 RIhyp 0.000 0.000 0.000 RIsi 0.000 0.000 0.000 WFhyp5 0.000 0.000 0.000 .WFhyp7 0.000 0.000 0.000 .WFhyp10 0.000 0.000 0.000 .WFhyp12 0.000 0.000 0.000 WFsi5 0.000 0.000 0.000 .WFsi7 0.000 0.000 0.000 .WFsi10 0.000 0.000 0.000 .WFsi12 0.000 0.000 0.000

Thresholds: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf92e5|t1 0.856 0.026 32.734 0.000 0.856 0.856 trf92e5|t2 1.848 0.047 39.638 0.000 1.848 1.848 trf93e5|t1 0.387 0.023 16.555 0.000 0.387 0.387 trf93e5|t2 1.467 0.035 41.504 0.000 1.467 1.467 trf104e5|t1 0.627 0.025 25.289 0.000 0.627 0.627 trf104e5|t2 1.751 0.043 40.789 0.000 1.751 1.751 trf105e5|t1 0.545 0.023 23.250 0.000 0.545 0.545 trf105e5|t2 1.552 0.035 43.955 0.000 1.552 1.552 trf100e5|t1 0.457 0.023 19.533 0.000 0.457 0.457 trf100e5|t2 1.415 0.034 41.572 0.000 1.415 1.415 trf101e5|t1 0.642 0.024 26.432 0.000 0.642 0.642 trf101e5|t2 1.412 0.034 41.792 0.000 1.412 1.412 trf102e5|t1 0.876 0.026 33.168 0.000 0.876 0.876 trf102e5|t2 1.714 0.043 40.108 0.000 1.714 1.714 trf103e5|t1 1.180 0.031 38.478 0.000 1.180 1.180 trf103e5|t2 1.984 0.053 37.547 0.000 1.984 1.984 trf66e5|t1 0.598 0.024 24.563 0.000 0.598 0.598 trf66e5|t2 1.568 0.038 41.634 0.000 1.568 1.568 trf92e7|t1 0.939 0.024 39.150 0.000 0.939 0.939 trf92e7|t2 1.882 0.042 45.326 0.000 1.882 1.882 trf93e7|t1 0.610 0.023 26.708 0.000 0.610 0.610 trf93e7|t2 1.556 0.029 52.764 0.000 1.556 1.556 trf104e7|t1 0.802 0.024 33.678 0.000 0.802 0.802 trf104e7|t2 1.730 0.036 47.753 0.000 1.730 1.730 trf105e7|t1 0.723 0.023 31.721 0.000 0.723 0.723 trf105e7|t2 1.698 0.032 52.453 0.000 1.698 1.698 trf100e7|t1 0.618 0.023 26.879 0.000 0.618 0.618 trf100e7|t2 1.449 0.025 57.431 0.000 1.449 1.449 trf101e7|t1 0.781 0.023 34.116 0.000 0.781 0.781 trf101e7|t2 1.455 0.026 55.740 0.000 1.455 1.455 trf102e7|t1 1.140 0.025 45.599 0.000 1.140 1.140 trf102e7|t2 1.837 0.040 45.436 0.000 1.837 1.837 trf103e7|t1 1.369 0.029 47.443 0.000 1.369 1.369 trf103e7|t2 2.086 0.053 39.197 0.000 2.086 2.086 trf66e7|t1 0.532 0.023 22.869 0.000 0.532 0.532 trf66e7|t2 1.554 0.033 47.082 0.000 1.554 1.554 trf92e10|t1 0.899 0.025 35.468 0.000 0.899 0.899 trf92e10|t2 1.844 0.041 45.400 0.000 1.844 1.844 trf93e10|t1 0.798 0.025 32.215 0.000 0.798 0.798 trf93e10|t2 1.732 0.035 49.671 0.000 1.732 1.732 trf104e10|t1 0.968 0.026 36.620 0.000 0.968 0.968 trf104e10|t2 1.868 0.041 45.560 0.000 1.868 1.868 trf105e10|t1 1.013 0.025 40.266 0.000 1.013 1.013 trf105e10|t2 1.853 0.037 50.389 0.000 1.853 1.853 trf100e10|t1 0.936 0.025 36.775 0.000 0.936 0.936 trf100e10|t2 1.704 0.035 49.239 0.000 1.704 1.704 trf101e10|t1 1.007 0.024 41.499 0.000 1.007 1.007 trf101e10|t2 1.715 0.034 50.413 0.000 1.715 1.715 trf102e10|t1 1.327 0.027 49.973 0.000 1.327 1.327 trf102e10|t2 2.014 0.049 41.518 0.000 2.014 2.014 trf103e10|t1 1.329 0.030 43.890 0.000 1.329 1.329 trf103e10|t2 2.212 0.062 35.889 0.000 2.212 2.212 trf66e10|t1 0.546 0.025 21.503 0.000 0.546 0.546 trf66e10|t2 1.538 0.033 47.018 0.000 1.538 1.538 trf92e12|t1 0.896 0.028 31.948 0.000 0.896 0.896 trf92e12|t2 1.824 0.039 46.462 0.000 1.824 1.824 trf93e12|t1 0.808 0.026 30.518 0.000 0.808 0.808 trf93e12|t2 1.684 0.036 47.033 0.000 1.684 1.684 trf104e12|t1 0.924 0.028 33.567 0.000 0.924 0.924 trf104e12|t2 1.959 0.047 42.043 0.000 1.959 1.959 trf105e12|t1 0.970 0.027 35.461 0.000 0.970 0.970 trf105e12|t2 1.901 0.041 46.652 0.000 1.901 1.901 trf100e12|t1 0.935 0.028 33.477 0.000 0.935 0.935 trf100e12|t2 1.880 0.043 43.693 0.000 1.880 1.880 trf101e12|t1 0.930 0.028 33.713 0.000 0.930 0.930 trf101e12|t2 1.840 0.040 46.212 0.000 1.840 1.840 trf102e12|t1 1.259 0.029 43.576 0.000 1.259 1.259 trf102e12|t2 2.023 0.049 41.377 0.000 2.023 2.023 trf103e12|t1 1.298 0.032 40.947 0.000 1.298 1.298 trf103e12|t2 2.161 0.058 37.347 0.000 2.161 2.161 trf66e12|t1 0.524 0.027 19.747 0.000 0.524 0.524 trf66e12|t2 1.486 0.032 45.801 0.000 1.486 1.486 trf11e5|t1 1.601 0.038 42.124 0.000 1.601 1.601 trf11e5|t2 2.627 0.094 27.994 0.000 2.627 2.627 trf19e5|t1 1.172 0.030 38.748 0.000 1.172 1.172 trf19e5|t2 2.218 0.062 36.044 0.000 2.218 2.218 trf24e5|t1 1.754 0.043 41.112 0.000 1.754 1.754 trf24e5|t2 2.634 0.099 26.702 0.000 2.634 2.634 trf30e5|t1 1.178 0.031 38.360 0.000 1.178 1.178 trf30e5|t2 2.162 0.057 37.982 0.000 2.162 2.162 trf34e5|t1 1.726 0.042 40.698 0.000 1.726 1.726 trf34e5|t2 2.632 0.098 26.922 0.000 2.632 2.632 trf77e5|t1 1.064 0.030 35.716 0.000 1.064 1.064 trf77e5|t2 2.066 0.056 37.209 0.000 2.066 2.066 trf11e7|t1 1.398 0.039 36.178 0.000 1.398 1.398 trf11e7|t2 2.398 0.064 37.308 0.000 2.398 2.398 trf19e7|t1 0.968 0.042 23.030 0.000 0.968 0.968 trf19e7|t2 2.228 0.058 38.495 0.000 2.228 2.228 trf24e7|t1 1.541 0.044 35.275 0.000 1.541 1.541 trf24e7|t2 2.443 0.069 35.292 0.000 2.443 2.443 trf30e7|t1 1.299 0.037 34.900 0.000 1.299 1.299 trf30e7|t2 2.334 0.069 33.700 0.000 2.334 2.334 trf34e7|t1 1.613 0.052 30.998 0.000 1.613 1.613 trf34e7|t2 2.944 0.136 21.612 0.000 2.944 2.944 trf77e7|t1 1.173 0.033 35.751 0.000 1.173 1.173 trf77e7|t2 2.220 0.063 35.362 0.000 2.220 2.220 trf11e10|t1 1.475 0.034 43.869 0.000 1.475 1.475 trf11e10|t2 2.231 0.061 36.713 0.000 2.231 2.231 trf19e10|t1 0.873 0.033 26.430 0.000 0.873 0.873 trf19e10|t2 2.017 0.046 44.207 0.000 2.017 2.017 trf24e10|t1 1.565 0.039 40.125 0.000 1.565 1.565 trf24e10|t2 2.453 0.079 30.873 0.000 2.453 2.453 trf30e10|t1 1.347 0.034 39.903 0.000 1.347 1.347 trf30e10|t2 2.333 0.071 33.027 0.000 2.333 2.333 trf34e10|t1 1.243 0.036 34.276 0.000 1.243 1.243 trf34e10|t2 2.287 0.066 34.755 0.000 2.287 2.287 trf77e10|t1 1.199 0.031 38.536 0.000 1.199 1.199 trf77e10|t2 2.185 0.063 34.474 0.000 2.185 2.185 trf11e12|t1 1.571 0.039 40.668 0.000 1.571 1.571 trf11e12|t2 2.581 0.094 27.482 0.000 2.581 2.581 trf19e12|t1 0.892 0.036 24.807 0.000 0.892 0.892 trf19e12|t2 2.084 0.054 38.678 0.000 2.084 2.084 trf24e12|t1 1.633 0.045 36.003 0.000 1.633 1.633 trf24e12|t2 2.633 0.105 25.069 0.000 2.633 2.633 trf30e12|t1 1.138 0.034 33.953 0.000 1.138 1.138 trf30e12|t2 2.168 0.061 35.811 0.000 2.168 2.168 trf34e12|t1 1.241 0.039 31.682 0.000 1.241 1.241 trf34e12|t2 2.442 0.080 30.417 0.000 2.442 2.442 trf77e12|t1 1.031 0.031 32.763 0.000 1.031 1.031 trf77e12|t2 1.968 0.053 36.881 0.000 1.968 1.968

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Fhyp5 0.000 0.000 0.000 .Fhyp7 0.000 0.000 0.000 .Fhyp10 0.000 0.000 0.000 .Fhyp12 0.000 0.000 0.000 .Fsi5 0.000 0.000 0.000 .Fsi7 0.000 0.000 0.000 .Fsi10 0.000 0.000 0.000 .Fsi12 0.000 0.000 0.000 .trf92e5 0.251 0.251 0.251 .trf93e5 0.189 0.189 0.189 .trf104e5 0.293 0.293 0.293 .trf105e5 0.202 0.202 0.202 .trf100e5 0.205 0.205 0.205 .trf101e5 0.124 0.124 0.124 .trf102e5 0.211 0.211 0.211 .trf103e5 0.417 0.417 0.417 .trf66e5 0.413 0.413 0.413 .trf92e7 0.213 0.213 0.213 .trf93e7 0.148 0.148 0.148 .trf104e7 0.257 0.257 0.257 .trf105e7 0.162 0.162 0.162 .trf100e7 0.164 0.164 0.164 .trf101e7 0.079 0.079 0.079 .trf102e7 0.170 0.170 0.170 .trf103e7 0.387 0.387 0.387 .trf66e7 0.383 0.383 0.383 .trf92e10 0.203 0.203 0.203 .trf93e10 0.138 0.138 0.138 .trf104e10 0.248 0.248 0.248 .trf105e10 0.152 0.152 0.152 .trf100e10 0.155 0.155 0.155 .trf101e10 0.068 0.068 0.068 .trf102e10 0.160 0.160 0.160 .trf103e10 0.380 0.380 0.380 .trf66e10 0.376 0.376 0.376 .trf92e12 0.198 0.198 0.198 .trf93e12 0.131 0.131 0.131 .trf104e12 0.243 0.243 0.243 .trf105e12 0.146 0.146 0.146 .trf100e12 0.148 0.148 0.148 .trf101e12 0.061 0.061 0.061 .trf102e12 0.154 0.154 0.154 .trf103e12 0.376 0.376 0.376 .trf66e12 0.371 0.371 0.371 .trf11e5 0.563 0.563 0.563 .trf19e5 0.071 0.071 0.071 .trf24e5 0.400 0.400 0.400 .trf30e5 0.693 0.693 0.693 .trf34e5 0.124 0.124 0.124 .trf77e5 0.798 0.798 0.798 .trf11e7 0.559 0.559 0.559 .trf19e7 0.061 0.061 0.061 .trf24e7 0.393 0.393 0.393 .trf30e7 0.690 0.690 0.690 .trf34e7 0.114 0.114 0.114 .trf77e7 0.796 0.796 0.796 .trf11e10 0.556 0.556 0.556 .trf19e10 0.056 0.056 0.056 .trf24e10 0.390 0.390 0.390 .trf30e10 0.689 0.689 0.689 .trf34e10 0.110 0.110 0.110 .trf77e10 0.795 0.795 0.795 .trf11e12 0.538 0.538 0.538 .trf19e12 0.016 0.016 0.016 .trf24e12 0.365 0.365 0.365 .trf30e12 0.675 0.675 0.675 .trf34e12 0.072 0.072 0.072 .trf77e12 0.786 0.786 0.786 RIhyp 0.353 0.024 14.651 0.000 1.000 1.000 RIsi 0.114 0.036 3.182 0.001 1.000 1.000 WFhyp5 0.397 0.023 17.166 0.000 1.000 1.000 .WFhyp7 0.404 0.017 23.231 0.000 0.930 0.930 .WFhyp10 0.375 0.024 15.882 0.000 0.845 0.845 .WFhyp12 0.406 0.018 22.320 0.000 0.902 0.902 WFsi5 0.323 0.039 8.292 0.000 1.000 1.000 .WFsi7 0.261 0.024 11.024 0.000 0.799 0.799 .WFsi10 0.287 0.023 12.532 0.000 0.871 0.871 .WFsi12 0.280 0.024 11.633 0.000 0.803 0.803

Scales y*: Estimate Std.Err z-value P(>|z|) Std.lv Std.all trf92e5 1.000 1.000 1.000 trf93e5 1.000 1.000 1.000 trf104e5 1.000 1.000 1.000 trf105e5 1.000 1.000 1.000 trf100e5 1.000 1.000 1.000 trf101e5 1.000 1.000 1.000 trf102e5 1.000 1.000 1.000 trf103e5 1.000 1.000 1.000 trf66e5 1.000 1.000 1.000 trf92e7 1.000 1.000 1.000 trf93e7 1.000 1.000 1.000 trf104e7 1.000 1.000 1.000 trf105e7 1.000 1.000 1.000 trf100e7 1.000 1.000 1.000 trf101e7 1.000 1.000 1.000 trf102e7 1.000 1.000 1.000 trf103e7 1.000 1.000 1.000 trf66e7 1.000 1.000 1.000 trf92e10 1.000 1.000 1.000 trf93e10 1.000 1.000 1.000 trf104e10 1.000 1.000 1.000 trf105e10 1.000 1.000 1.000 trf100e10 1.000 1.000 1.000 trf101e10 1.000 1.000 1.000 trf102e10 1.000 1.000 1.000 trf103e10 1.000 1.000 1.000 trf66e10 1.000 1.000 1.000 trf92e12 1.000 1.000 1.000 trf93e12 1.000 1.000 1.000 trf104e12 1.000 1.000 1.000 trf105e12 1.000 1.000 1.000 trf100e12 1.000 1.000 1.000 trf101e12 1.000 1.000 1.000 trf102e12 1.000 1.000 1.000 trf103e12 1.000 1.000 1.000 trf66e12 1.000 1.000 1.000 trf11e5 1.000 1.000 1.000 trf19e5 1.000 1.000 1.000 trf24e5 1.000 1.000 1.000 trf30e5 1.000 1.000 1.000 trf34e5 1.000 1.000 1.000 trf77e5 1.000 1.000 1.000 trf11e7 1.000 1.000 1.000 trf19e7 1.000 1.000 1.000 trf24e7 1.000 1.000 1.000 trf30e7 1.000 1.000 1.000 trf34e7 1.000 1.000 1.000 trf77e7 1.000 1.000 1.000 trf11e10 1.000 1.000 1.000 trf19e10 1.000 1.000 1.000 trf24e10 1.000 1.000 1.000 trf30e10 1.000 1.000 1.000 trf34e10 1.000 1.000 1.000 trf77e10 1.000 1.000 1.000 trf11e12 1.000 1.000 1.000 trf19e12 1.000 1.000 1.000 trf24e12 1.000 1.000 1.000 trf30e12 1.000 1.000 1.000 trf34e12 1.000 1.000 1.000 trf77e12 1.000 1.000 1.000

S4 Model fit: (We have included here the change in CFI, TLI and RMSEA compared to the S3 model) Comparative Fit Index (CFI) 0.956 (>0.95) Change in CFI: 0. (decrease) - worse fit Tucker-Lewis Index (TLI) 0.955 (>0.95) Change in TLI: 0. (decrease) - worse fit RMSEA 0. (≤ 0.06) Change in RMSEA: 0. (increase) - worse fit 90 Percent confidence interval - lower 0. 90 Percent confidence interval - upper 0.
SRMR 0. (≤ 0.08) Change in SRMR: 0. (increase) - worse fit

# summary(semTools::compareFit(RICLPMt_multi_hyp_S3.fit, RICLPMt_multi_hyp_S4.fit, nested = TRUE)) #† indicates the best fitting model

As all fit indices changed by more than 0.01 - we cannot accept this measurement model.

Work by Katherine N Thompson

katherine.n.thompson@kcl.ac.uk

RICLPM with factor measurement model for social isolation and ADHD symptoms in childhood

Cronbach alpha for social isolation items

Load data

All RI-CLPM models displayed here

Multiple indicator RI-CLPM

Mother report RI-CLPM: Inattention and social isolation

RICLPM_multi_inat_S1: Inattention step 1

RICLPM_multi_inat_S2: Inattention step 2

RICLPM_multi_inat_S3: Inattention step 3

RICLPM_multi_inat_S4: Inattention step 4 - Full model

Multiple indicator RI-CLPM: Hyperactivity and social isolation

RICLPM_multi_hyp_S1: Hyperactivity/Implsivity step 1

RICLPM_multi_hyp_S2: Hyperactivity step 2

RICLPM_multi_hyp_S3

RICLPM_multi_hyp_S4: Hyperactivity step 4 - Full model

Multiple indicator RI-CLPM: ADHD and social isolation

RICLPM_multi_adhd_S1: ADHD step 1

RICLPM_multi_adhd_S2: ADHD step 2

RICLPM_multi_adhd_S3: ADHD step 3

RICLPM_multi_adhd_S4: Total ADHD symptoms step 4 - Full model

Teacher report RI-CLPM: Inattention and social isolation

RICLPM_multi_inat_S1: Inattention step 1

RICLPMt_multi_inat_S2: Inattention step 2

RICLPMt_multi_inat_S3: Inattention step 3

RICLPMt_multi_inat_S4: Inattention step 4 - Full model

Exported tables for this model

Multiple indicator RI-CLPM teacher report: Hyperactivity and social isolation

RICLPMt_multi_hyp_S1: Hyperactivity/Implsivity step 1

RICLPMt_multi_hyp_S2: Hyperactivity step 2

RICLPMt_multi_hyp_S3

RICLPMt_multi_hyp_S4: Hyperactivity step 4 - Full model