I am working on an EFA, and eventually an SEM, with weighted, multiply imputed data with ordinal variables. I have a total of 26 ordinal variables that I expect to yield up to 6 factors. Right now I'm working with a subset of 6 of those variables that should load on 2 factors to figure out the code.
I have run the EFA both with just imputation 1 with the weighting variable using ANALYSIS: TYPE = EFA 1 2;.
I have also run it with all 5 imputed data sets and the weighting variable using MODEL: f1 - f2 BY <variable names> (*1);.
My current plan is to use the standard EFA with 1 imputation to examine the eigenvalues, make a scree plot, and get a feel for how the factors are loading, and then confirm my results with the one with 5 imputations by running all the the possible contenders and comparing model fit.
Here are my questions:
The imputed analysis yields a monte-carlo type set of model fit statistics. How should I interpret these?
The imputed analysis gives me a correlation of 0.91 between F1 and F2, while the 1 imputation EFA gives me a correlation of 0.319. Why are they different?
I requested residual output for the imputed analysis, and there is a note that says NOTE: These are average results over 10 data sets. However, I only have 5 imputed data sets. What is happening there?