CFA: JASP doesn't show p-values and standard errors of factor loadings

Comments

• patc3

  December 2023

  No it's not because the assumptions are not met. Can you show a screenshot of your analysis and your output?
• patc3

  December 2023

  OK I could reproduce your situation with a dataset that ships with JASP in the data library (14- SEM: Grade point average):

  

  I did a little bit of digging by running the same analysis directly in R:

  setwd("c:/Program Files (x86)/JASP/resources/Data Sets/Data Library/14. SEM/")
  df<-read.csv("Grand Point Average.csv")
  library(lavaan)
  
  "
  f1 =~ NA*gpa1+gpa2+gpa3
  f2 =~ NA*gpa4+gpa5+gpa6
  f =~ NA*f1+f2
  f1~~1*f1
  f2~~1*f2
  f~~1*f
  " |> cfa(df)
  

  What JASP doesn't show you is this warning:

  Warning message:
  In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
    lavaan WARNING:
      Could not compute standard errors! The information matrix could
      not be inverted. This may be a symptom that the model is not
      identified.
  

  JASP doesn't show this warning (rightfully or not) because it's "just" a warning, not an error. And lavaan says the model converged:

  > analysis@optim$converged
  [1] TRUE
  

  lavaan suggests the model may not be identified (i.e. trying to estimate too many parameters for the number of variables included). Basically your model might be too complex for the number of variables--but I did try to add more items in each factor, and was still getting this warning (and no test for the factor loadings).

  Looking at my factor loadings, the correlations between my items seem to be quite low. Also I was getting another warning saying I had negative latent variances, so perhaps the scores don't vary much across people. These are the kind of things you can look for in your own analysis (particularly the size of the factor loadings). Also you need to make sure you have enough items in each factor (like 3 at a bare minimum).