

Model fit for ML models with categori... 

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Dear Linda and Bengt: I am estimating a model with several categorical outcomes. It is my understanding that, to obtain odds ratios, I need to use the MLR estimator rather than the WLSMV estimator. (The WLSMV estimator will give me probit regression coefficients rather than logistic regression coefficients, correct?) If this is accurate, then the only model fit indices available to me are the 2LL, the AIC, and the BIC. One of my coauthors wants to know how well the model fits the data, but these indices cannot tell me that  correct? Is there any way for me to tell how well the model fits? Can anything be done with the 2LL, AIC, or BIC to evaluate the fit of a single model to the data? I'm worried that reviewers will criticize me for not making a statement about overall model fit before proceeding to test the signficance of specific paths. Thanks again for your help. Seth 


If your model has positive df for chisquare when running WLSMV (probit) then it has some leftout paths. If so, you can use ML (logit) and let those paths be free to get the H1 model to test your H0 model against. 


I am running SEM with categorical variables (some binary; some ordinal). I am using ML method in SAS to fit the model. I was wondering whether I should use polychoric correlation matrix as my input to the model (instead of the default, which I assume is Pearson's correlation). Or is the ML method robust enough even when all variables are categorical? Thank you! 


This Discussion forum is for Mplus users. Regular ML is not appropriate for polychoric correlation matrices. You can also try SEMNET. 

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