I have a problem and hope there is someone here that can help.
I have written an article that uses a BRMS multivariate multilevel analyses to track mental model growth of three separate complex skills in three conditions, acros 2 schools over 3 time points.
However, my supervisors are not Bayesians and are frankly a little scared.
Is there anyone here that can give me feedback, me and my supervisors could finally get on with our lives.

Thank you very much, i hope someone can find the time

How about posting the paper as a preprint on e.g. OSF or psyarxiv? It would be easier to provide feedback if it was availableâ€“and Iâ€™d be happy to take a look (but agree with Paul that reading the whole paper might be a lot to ask for.) I think OSF has some kind of commenting function now so thatâ€™s a plus as well.

Well, OSF did not let you edit end gdocs messed with the layout so here is a word link that should work. I only posted the part that needs a Bayesian eye ;)

Thereâ€™s a lot of detail there, but right off the bat I would suggest that you describe the model in more detail (right now you spend a lot [too much] space on things like convergence, but I didnâ€™t see what the response variables and predictor variables were, and what the model actually is).

I see that you have compared the same model to itself across different numbers of samples. Thereâ€™s no reason to do that.

Generally, I would spend quite a bit of time on clarity of presentation.

did you really use The Gelman and Rubin (1992) diagnostic or the more recent version of split-Rhat diagnostic implemented in Stan (and then the correct reference would be BDA3)?

130 000 iterations is awfully lot for dynamic HMC in Stan. What is n_eff? Later tables show â€śEff.Sampleâ€ť which I guess is n_eff. If n_eff is 10 000 with 4x80000 post-warmup iterations , you may have problems with your model.

You are showing way too many digits of LOOIC. Given SE you would need to show max 3 digits, and thus there is no statistical difference between 4000 and 130000 iterations.

It would be good to include some posterior predictive checking (see, e.g. ppc in bayesplot)