#to create 2factor check list model
AssesCheck.model <- '
Check1 =~ A_Check1 + A_Check5 + A_Check6 + A_Check7 + A_Check10
Check2 =~ A_Check2 + A_Check3 + A_Check4 + A_Check8 + A_Check9 + A_Check11 + A_Check12 
'
#do CFA analysis of model
AssessCheck_fit <- cfa (AssesCheck.model, data=NZSCoAVI, ordered=c("A_Check1", "A_Check5", "A_Check6", "A_Check7", "A_Check10", "A_Check2", "A_Check3", "A_Check4", "A_Check8", "A_Check9", "A_Check11", "A_Check12"), estimator="wls")
#get results of cfa
summary (AssessCheck_fit, standardized=TRUE)
fitmeasures(AssessCheck_fit)

#to get gamma hat fit index
library(semTools)
moreFitIndices(AssessCheck_fit)

#to get x2/df ratio and p-value-thanks to Terry Jorgensen UvA (amsterdam)
fm <- fitMeasures(AssessCheck_fit)
fm[["chisq"]] / fm[["df"]]
fm["pvalue"] 


#to create Bartlett factor scores
fit.AC1 <- sem('Check1 =~ A_Check1 + A_Check5 + A_Check6 + A_Check7 + A_Check10', data = NZSCoAVI)
fsAC1 <- lavPredict(fit.AC1, fsm = TRUE, method = "Bartlett")
fit.AC2 <- sem('Check2 =~ A_Check2 + A_Check3 + A_Check4 + A_Check8 + A_Check9 + A_Check11 + A_Check12', data = NZSCoAVI)
fsAC2 <- lavPredict(fit.AC2, fsm = TRUE, method = "Bartlett")

#create a data frame with the 5 bartlett factor scores 
FS.Data <- data.frame(Test = fsAC1, Interactive = fsAC2)
round(FS.Data, digits = 2)