Seemingly Unrelated Ordered Probit PreviousNext
Mplus Discussion > Categorical Data Modeling >
 Thomas Scotto posted on Monday, May 21, 2012 - 2:23 am

I'm having some problems getting my head around how MPLUS can be jigged to run a Seemingly Unrelated Oredered Probit. Suppose for simplicity's sake, I have two ordinal dependent variables "red" and "blue" that I wish to regress on co-variates "c1" and "c2"

I don't believe a model statement like this gives me what I need as it is the errors in the ordinal dependent variables that necessitate correlation:

red on c1 c2;
blue on c1 c2;

Am I correct in this assumption?

What I am thinking might be a workaround is to designate red and blue as latent variables and then proceed with the estimation?

f_red by red@1;
f_blue by blue@1;

f_red on c1 c2;
f_blue on c1 c2;

Questions: 1) Is this line of reasoning correct?

2) Are there any other workarounds? I get too many integration points with the MLR (my preferred) estimator and non convergence with WLSMV.


 Linda K. Muthen posted on Monday, May 21, 2012 - 11:22 am
For WLSMV, you would say:

red on c1 c2;
blue on c1 c2;
red WITH blue;

For MLR, you would say:

red on c1 c2;
blue on c1 c2;
f BY red@1 blue;
f@1; [f@0];

where the residual covariance parameter is found in the free factor loading for blue.
 ssp2yssp12 posted on Wednesday, August 08, 2012 - 2:20 am
Hello: I am running a seemingly unrelated ordered probit model, with two ordinal dependent variables. I have run various nonnested models and would like to compare them. Is there a particular model fit/goodness of fit statistic in the output that I should be looking at to compare models?

Thank you,
 Linda K. Muthen posted on Wednesday, August 08, 2012 - 9:31 am
If you use maximum likelihood and the probit link, you could look at BIC.
 ssp2yssp12 posted on Wednesday, August 08, 2012 - 10:09 am
Thank you! However, what if I am using the MLMV or WLSMV estimators, rather than ML?
 Linda K. Muthen posted on Wednesday, August 08, 2012 - 11:51 am
All estimators that start with ML are maximum likelihood. You will not get BIC with WLSMV.
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