Estimating Bayes factors from minimal ANOVA summaries for repeated-measures designs
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In this paper, we develop a formula for estimating Bayes factors from repeated measures ANOVA designs. The formula, which requires knowing only minimal information about the ANOVA (e.g., the F -statistic), is based on the BIC approximation of the Bayes factor, a common default method for Bayesian computation with linear models. In addition to several computational examples, we report a simulation study in which we demonstrate that despite its simplicity, our formula compares favorably to a recently developed, more complex method that accounts for correlation between repeated measurements. Our method provides a simple way for researchers to estimate Bayes factors from a minimal set of summary statistics, giving users a powerful index for estimating the evidential value of not only their own data, but also the data reported in published studies.
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