I am a grad student at Aarhus University, Denmark. I am fairly new with SEM but have read as much literature as I could possibly get my hands on. I am working in AMOS. I have attached a picture of the AMOS setup.
I have set up a model containing 3 latent factors, each with 6 observed variables attached.
Problem: When I do a CFA of each latent variable individually, that data is both univariate normal distributed and multivariate normal distributed (Mardia's test). Then when I set up the 3-factor CFA validity test with covariance between the factors, the data is suddenly multivariate non-normal distributed. I am wondering how to explain this phenomenon? The problem is, that while the individual CFAs with normal dist. have satisfactory CFI and RMSEA the combined CFA (with non-normality) has very low Bollen-Stine p-value (0,005). I have a hard time figuring out, how three individually good fitting models can become so bad fitting combined?
Additionally: Does it make sense to analyse the way described above? Fitting each latent factor with observed variables first, then combining the factors in one model.
Thank you for your time and any input!
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