I estimated two models with type = complex and estimator = MLR. I want to compare their chi-square. My problem is that I have to estimate the same model with estimator = ML to compare the chi-square (as indicated in one of your appendix) but I cannot do that because it is not possible to estimate a model ML and complex. What can I do to still obtain a chi-square difference test that is correct?
I need to compare a two-level MIMIC model to the same model with random slope. Using MLR (with Algorithm=integration) it gives me the message THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE (.....) PROBLEM INVOLVING PARAMETER 23.
THE MODEL ESTIMATION TERMINATED NORMALLY
I heard that it is not a relevant error, but then Mplus can't calculate Chi-square, CFI, RMSEA, only IC test (BIC, AIC). THe WLS estimation doesn't give this error, but I can't use it with TYPE = TWOLEVEL RANDOM, for the computetion of the random slopes.
I could compare the IC but It is a pretty weak test.
I think the problem is taht the baseline model does not converge with the MLR. How can I overcome it?