Yep! I’m following the recommendations and guidelines from this paper, which say to look at the probability of direction to see if your effect exists, and then gauge its meaningfulness with the ROPE+HDI method. Currently using the bayestestR package to accomplish both. I’ll be reporting in terms of odds ratios instead of the coefficient estimates probably.
What is less clear/straightforward to me though is how to evaluate a model once you have one (or a few competing ones). This page from the bayestestR site gives me a way to compare my models with Bayes factors, but I’m not sure that alone is going to be enough to justify my models to a peer reviewer. Or will it? I was looking for some way to quantify the models’ performance too (a la Wald chi-square change and Nagelkerke/McFadden R^2).
Also investigating this guide on Posterior Predictive Checking…but I’m not sure how related this is to my question. Undoubtedly the hardest part of learning Bayes for me is finding a set of “best practice guidelines”. There doesn’t seem to be any standardized procedure for how to carry out an analysis as of yet.