this is my first SEM I've done so I'm still getting my head around many of the concepts.
So I think I've created a hierarchical model of IQ (generaliq), with the latent variables: verbal_letter, matrix and rotate. verbal_letter was two latent vars lumped together due to a correlation >1 error. It's done using a rather odd data-set (not usual IQ measures, rather questions that are meant to link to underlying concepts, taken from a SAPA questionnaire) but anyhow it appears to be working.
dataset info here: http://vincentarelbundock.github.io/Rdatasets/doc/psych/iqitems.html
My relevant code is:
library(lavaan) library(semPlot) iqitems <- read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/psych/iqitems.csv') iq.model6 <- 'verbal_letters =~ reason.4 + reason.16 + reason.17 + reason.19 + letter.7 + letter.33 + letter.34 + letter.58 matrix =~ matrix.45 + matrix.55 rotate =~ rotate.3 + rotate.4 + rotate.6 + rotate.8 rotate.3 ~~ rotate.4 generaliq =~ verbal_letters + matrix + rotate' iq.fit6 <- cfa(model = iq.model6, data = iqitems) summary(iq.fit6, standardized = TRUE, fit.measures = TRUE)
When I summarise my model, all seems fine in a model fit sense CFI and TLI >0.9, RMSEA and SRMR >0.05, I have enough DF for identification.
It's this part of the summary I'm concerned about:
Latent Variables: generaliq =~ verbal_letters 1.000 0.930 0.930 matrix 1.344 0.132 10.164 0.000 0.959 0.959 rotate 0.620 0.082 7.575 0.000 0.484 0.484
Are the 0.930 and 0.959 problematic? If so why and what can I do?
Otherwise if there are any other big problems I'd appreciate being made aware. For context, I'm doing this for a stats module in undergraduate psychology so the standard is not really high. I basically need to (amongst many other things) run an analysis of my own choice and report on it with a theoretical question, technical details and graphical analysis.
I've done the following diagram
semPaths(object = iq.fit6, whatLabels = "std", edge.label.cex = 1, layout = "tree", rotation = 2, what = "std", edge.color = "navy")
If anyone has any feedback on it, or advice on how to generally report a SEM, I would really appreciate that too. Previously I've been doing regressions like LMER and GLM so this is rather different.
Thanks a lot!