I was wondering if there's any study (or rule of thumb) for the minimal number of clusters for a multilevel model with Bayesian estimator? I assume, the criterion here would be different from what is suggested for when the "frequentest" estimators are used (e.g. Muthe´n 1989; Meuleman and Billet 2009).
Specifically, I am fitting a model using a longitudinal ANES datafile with 19 time-points and also the same model restricted to 13 time-points. The model seems to fit fine (although there's obviously no fit indices available) and confirms the hypotheses, but I would like to have some support for the # of clusters used.
The same model estimated by WLMSV or ML methods doesn't converge.
A quick follow-up on my own post. When I check graphs for my model (plot2), I only get the mean, median and mode of (I assume) posterior distributions. These are given for each parameter in the model. Would there also be a way to get the posterior predictive p-value for the model (observed-replicated) similar to what you describe in a "Short Courses Topic 9?" (e.g. slide 56).