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Number of clusters for Bayesian estim... |
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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 |
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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, Dmitriy |
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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. |
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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? |
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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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