When to use Bayesian mixed model
I have a repeated measures experiment with three conditions, multiple repetitions per condition, and 15 subjects. The standard in my field is to average the results per condition (disregarding the number of trials) and compare the three conditions (45 data points overall). Using this method with repeated-measures Bayesian analysis in JASP, I get a somewhat unsatisfactory BF (BF01 = 2.79 in support of H0). I thought that maybe this is because the results don't converge well due to the number of subjects. When I use a Bayesian mixed model (using BayesFactor in R) on the same data, with the subject intercept as the random factor, I got a much stronger support of H0 (BF01 = 20+). Intuitively, this seems like a reasonable approach because the BF will converge better if I take each trial as a data point instead of each average (2250 data points). However, while I know there are various justification for using mixed models, I couldn't find any justification that relates to this notion. Is it justified? Is there any article that supports my approach?