

Weighted, Multiply Imputed Data 

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Hello, 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 montecarlo 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? Thank you! 


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