Number of clusters for Bayesian estim... PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 Dmitriy Poznyak posted on Wednesday, July 06, 2011 - 3:58 am
Dear Linda and Bengt,

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.

Thank you,
 Dmitriy Poznyak posted on Wednesday, July 06, 2011 - 6:22 am
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).

Thank you,
 Bengt O. Muthen posted on Wednesday, July 06, 2011 - 7:14 am
See the multilevel section of

Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report.

This shows that you can get good results for only 10 clusters.

There is no PPP approach available yet for multilevel.
 Dmitriy Poznyak posted on Wednesday, July 06, 2011 - 7:29 am
Thanks for the prompt reply, Dr. Muthen

How would I then judge whether the model fits alright? Can I rely on the graphical output and conclude by eyeballing the distribution?
 Bengt O. Muthen posted on Wednesday, July 06, 2011 - 12:46 pm
You may want to work with competing models. So for instance, if you fit a linear model, also fit a quadratic to see if the quadratic parts are needed.
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