I was wondering if anyone had some insight on which measure of model fit is the most appropriate (in reference mostly to the 5 different AIC and BIC values output by OpenMX) - both in general situations and in my particular situation. This is most important to us since in our models they aren't agreeing on an answer.
For my particular problem, we're looking at 8 variables (in a twin modeling framework, but we're looking at non-twin models as well) and these variables are split into 4 closely correlated pairs (correlations around .5) that are loosely correlated with one another.
We're fitting latent factors (common pathway) to the 4 pairs and then looking at 3 structures - one where we allow the 4 factors to be correlated, one where there's an overall factor driving all 4, and one where the latent factor is just another common factor that contributes to all 8 variables.
Are there any rules of thumb about when to use the particular AIC and BIC values given by OpenMX? Is there a particular measure that should be used in the type of model comparison outlined above?