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Analysis of variance (ANOVA) is widely used to assess the influence of one or more experimental (or quasi-experimental) manipulations on a continuous outcome. Traditionally, ANOVA is carried out in a frequentist manner using p values, but a Bayesian alternative has been proposed. Assuming that the proposed Bayesian ANOVA is closely modeled after its frequentist counterpart, one may be surprised to find that the two can yield very different conclusions when the design involves multiple repeated-measures factors. We illustrate such a discrepancy with a real data set from a two-factorial within-subject experiment. For this data set, the results of a frequentist and Bayesian ANOVA are in a disagreement about which main effect accounts for the variance in the data. The reason for this disagreement is that frequentist and the proposed Bayesian ANOVA use different model specifications. As currently implemented, the proposed Bayesian ANOVA assumes that there are no individual differences in the magnitude of effects. We suspect that this assumption is neither obvious to nor desired by most analysts because it is untenable in most applications. We argue here that the Bayesian ANOVA should be revised to allow for individual differences. As a default, we suggest the standard frequentist model specification but discuss a recently proposed alternative and provide guidance on how to choose the appropriate model specification. We end by discussing the implications of the revised model specification for previously published results of Bayesian ANOVAs.
Bergh et al. (Sat,) studied this question.