I'm trying to implement the Satorra-Bentler strictly positive chi-square difference test as described in Mplus Web note 12 for a factor mixture model with 4 classes. I have followed the procedure outlined in the web note except that to prevent Mplus from updating the start values, I have set convergence, mconvergence, logcriteria and rlogcriteria parameters to large values. Despite all this, Mplus takes a 2nd iteration in the EM algorithm regardless of how high I set the convergence parameters. If I put in start values with more then 3 significant digits, like 6 significant digits, which is considerably more work then using the svalues feature, Mplus still takes a 2nd iteration even though almost nothing changes. Is there something else that has to be set?
I tried setting miter=1 and then Mplus tells me that an insufficient number of E steps have been taken and I don't get the MLR scaling factor that I need to compute the strictly positive test. I will send you the example and data.
I am running into the same problem as presented above with estimating an m10 model. I have tried increasing the convergence, but there are iterations in the 'gradient' and 'quasi-newton' sections. Is there any more news on this issue? Thank you.
I am also running into this same problem with type=random and algorithm=integration. When I set miter=1, it tells me "THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN INSUFFICIENT NUMBER OF E STEPS. INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED" and it does not give me any loglikelihood or scaling factors.
Was there a resolution to this problem that would allow me to get the scaling factor for the M10 model?
Dear Muthéns, I performed a LMS model with 2 groups (type=mixture random; algorithm=integration). Subsequently, I computed a SB scaled Chi square difference test to compare a model without the interaction term (Model 0) to a model with the interaction term (Model 1). Thus the result of the SB scaled Chi square difference test was negative, I tried to estimate a third model (Model M10). I followed web note 12, example 1. My models are: M0 – Model without interaction term, output: svalues; M1 – Model with interaction term in each group; M10 – 1) Model produced by svalues; 2) adding the interaction term in each group (f3 on f1xf2) Question: Is this the right procedure for LMS models? I am asking because the scaling correction factor of M10 is smaller than the scaling correction factor of the M1 model – thus the chi-square is still negative. Mplus is fixing the interaction term automatically to zero. Thank you very much.