I am an undergraduate psychology student and I've been doing basic statistical analysis for professors, post-grad students, etc for their work. I use R for almost everything
I've recently faced myself with exploratory and confirmatory factor analysis for psychological test validation, etc. When talking about EFA, everything went well.
The scale has 10 items, Likert 1 to 5.
I'm going straight to my question:
When I'm creating CFA model in lavaan, there is possibility to create latent factors and observed variables relationship (=~), but you can also create covariance relationship (~~). I tried to explain my professor that we could insert those covariances in the model to improve model fit, etc. But she would not understand (even me, I don't completely comprehend it), because she was saying that the factors on EFA are already created based on item correlations in the instrument, and then it wouldn't make sense to assign those correlations.
What I did is, I ran three models (single factor, two factor and three factor) and also those three including two correlations observed in polychoric correlation matrix. So, in total, 6 CFA models. The ones with the correlations in the model performed better based on fit indices (CFI, RMSEA, SRMR, etc)
Could someone clarify it to me? Is it important to put those covariances in CFA model? Is it good practice? What does it mean in "human/eli5 language"?
If I forgot to give some information, feel free to ask me.
Thanks in advance, love this community!