I am using the knownclass option to estimate what is essentially a multiple groups CFA model and would like to compare the fit with a model where groups are ignored (fully constrained). Everything is working fine, but I am unsure how to compare the fit for the two models. I would like to use the DIC or Bayes Factor, but the former doesn't seem to be supported with mixture models, and I cannot find the posterior model probability for the latter. I was wondering if you had any suggestions. Thanks so much for any thoughts.
Thanks Bengt. I originally tried to fit the constrained and unconstrained models using ML, but couldn't get convergence, so I thought I would give the Bayesian approach a go. Both the fully constrained (Kenyan and American kids together) and separate groups analyses produce estimates, with nice looking trace plots for the parameters of interest. What I would like to do now is compare the fit of the models to see whether I have invariance or not.
Please let me know if you have more questions. Thanks.
So you want to do a 2-group Bayes analysis and check invariance. Why not compare the PPP for the no-invariance and invariance models? It doesn't test the difference specifically, but should be helpful. New tools are coming for such multi-group Bayes comparisons.
But you should be able to get ML to converge if Bayes converges. At least by using SVALUES to take the Bayes ending values into the ML run as start values.
Holmes Finch posted on Thursday, October 20, 2011 - 11:07 am
Thanks very much, Bengt. I will take a look at the PPP values for sure. I can try the SVALUES with ML approach as well. That might well help with convergence. I look forward to the multi-group Bayes stuff when it comes out.
an-tsu chen posted on Wednesday, March 12, 2014 - 12:42 am
I think my question should be classified under this thread. When conducting Bayesian CFA with categorical variables, the Mplus result seems not to show BIC value, making me unable to use this index to do model comparison. Does that mean it is not appropriate to consider BIC when data are categorical? Moreover, to the best of knowledge, PP p-value could also be used to do model comparison with categorical data, even though it is a goodness-of-fit index. Is that correct?