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Hello, I did an exploratory factor analysis in SPSS which suggested 4 clear factors. One factor in particular has four items with factor loadings all greater than .79. However, when I plug these items in to CFA in MPlus, only two items have high factor loadings (>0.83) and the two others are low (<0.36). Can you explain this? Thanks. 


The 4factor SPSS analysis determined the loadings for the 4 items by not only the correlations among the 4 items but also by their correlations with other items. If you redo the SPSS analysis with only those 4 items you probably get more similar results to the CFA. Assuming that you are using estimators that are the same or similar. 


Thank you for your reply. I redid the SPSS with only those 4 items, and 3 loaded at > 0.98 and one loaded at about 0.80. I'm not strong in statistics, and I don't really know what you mean by estimators, but I told SPSS to use maximum likelihood with PROMAX rotation. I guess what I really need to know is whether I should bank on Mplus's CFA analysis  and therefore assume that those four items are not loading on the same factor. Thanks again! 


If you treat the items as continuous (the Mplus default) and use the ML estimator in both programs, the loadings are going to be exactly the same since CFA with 1 factor is the same as EFA with 1 factor. So something else is going on that I can't see. Note also that when you do the CFA and want the results in the same metric as the EFA, fix the factor variance at 1 and free the first factor loading. A 1factor CFA with some large and some small loadings does not invalidate the hypothesis of a single factor. That should instead be determined by the chisquare test of model fit. 


I have categorical items and I'm using WLSMV in Mplus. Do you think that could be accounting for the difference? Should I change the estimator in either SPSS or Mplus? Thank you so much for your help. 


I don't know if SPSS can handle categorical items. You can't compare results from one program treating the items as continuous and therefore using a linear factor model with results from another program treating the items as categorical and therefore using a nonlinear (probit/logit) factor model. You will be fine using WLSMV in Mplus treating the items as categorical. 


Thank you. Would you confirm that I should think of what I call "ordinal" level items as "categorical" in Mplus? 


That's right. 


Thanks for your assistance! 


Hi, I've got a dataset with 23 items (rated 17 scale). I've run an EFA in SPSS (prinicipal axis factoring, varimax rotation, 2 factors extracted) and found that 2 factors accounted for well over half the variance in the dataset and the loadings of the items make perfect theoretical sense. When I got to test the model in CFA (2 factor model), the fit indices (CMIN, RMSEA, CFI, NFI, etc.) are all really bad. I am wondering if you might be able to help me understand what might be happening (or point me to an article that could)? Thanks! Steve 


Your SPSS EFA does not give you standard errors of the rotated factor loadings and therefore does not give you information about significant crossloadings which when fixed at zero in CFA causes misfit. Mplus EFA will give you this information. You also don't have to impose the strong structure of the CFA  for more on that I suggest that you read about the new idea of "ESEM": Marsh, H.W., Muthén, B., Asparouhov, A., Lüdtke, O., Robitzsch, A., Morin, A.J.S., & Trautwein, U. (2009). Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students’ Evaluations of University Teaching. Forthcoming in Structural Equation Modeling. and also the AsparouhovMuthen article both on the Mplus web site at http://www.statmodel.com/esem.shtml 


Hello, I have a strange output for my EFA with categorical items using a complex data set. My models run, but the chi square result is *******. Could you please explain this result? Thank you. Heidi 


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