Mixture analyses using Akaike Information Criterion

Hi guys,
Just wondered if you could shed some light on interpreting results of a mixture analyses using Akaike Information Criterion.
I am trying to fit a model to my data to determine whether within the sample there are 2, 3 or 4 potential groupings.
I have been using PAST software for this which uses the AICc formulae. How do I determine which is the greatest fitting model, is it by using the Log l.hood value or the AIC value.
And what am I looking for in terms of value to demonstrate greatest fit?
I have attached a sample of my results to demonstrate both the readout AIC and l.lhood values of which I am dealing with.
Cheers all


New Member
It looks like PAST offers very little in terms of fit statistics. Log likelihood is not appropriate for this sort of comparison. The AIC can be used (lower = better), but it doesn't function well under all circumstances. Theory and group meaningfulness/utility should play major roles in determining how many groups you're working with. Aside from that, you'd use a variety of fit statistics if they were at your disposal, but if you're stuck with PAST, then the AIC is it.

A very relevant paper that discusses these things:

Nylund, K. L., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535-569.