Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

Abstract

Nested data structures, in which conditions include multiple trials and are fully crossed with participants, are often analyzed using repeated-measures analysis of variance or mixed-effects models. Typically, researchers are interested in determining whether there is an effect of the experimental manipulation. These kinds of analyses have different appropriate specifications for the null and alternative models, and a discussion on which is to be preferred and when is sorely lacking. van Doorn et al. (2021) performed three types of Bayes factor model comparisons on a simulated data set in order to examine which model comparison is most suitable for quantifying evidence for or against the presence of an effect of the experimental manipulation. Here, we extend their results by simulating multiple data sets for various scenarios and by using different prior specifications. We demonstrate how three different Bayes factor model comparison types behave under changes in different parameters, and we make concrete recommendations on which model comparison is most appropriate for different scenarios.