That should work with regular latent class modeling as long as you have a sufficient sample size. I am not aware of special methodology for this, but please educate me if you are.
mpduser1 posted on Tuesday, April 02, 2013 - 12:05 pm
I am not aware of a special methodology either; but know that your team is at the forefront of LCA software and modeling and so didn't know if it was on your radar screen.
My thought was that the C L-L framework might open up some interesting possibilities for smaller sample sizes and response assumptions. I'm sure the interpretation of model parameters would be much more difficult, however.
I was also interested in the C L-L more generally, which Linda indicates is not currently available.
This reminds me of case-control data (where you use all cases and a random sample of non-cases) and weighted logistic regression, which I think can be done in Mplus although I haven't looked into it yet (there is an old Satorra-Muthen tech note on it). But that's not in the context of LCA.
You are right Bengt, it is used also for that. Can you give me the exact reference. I couldn't find on the website.
I was also wondering whether there might be some other solution to the problem. I would use bootstrapping for instance, but in my analysis I have an LCA with covariates and I have to use Montecarlo integration. Would increase the number of initial stage starts and final stage optimizations create more robust analyses under the condition of small classes