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In this paper, I develop a formula for estimating Bayes factors directly fromminimal summary statistics produced in repeated measures analysis of variancedesigns. The formula, which requires knowing only the $F$-statistic, the numberof subjects, and the number of repeated measurements per subject, is based onthe BIC approximation of the Bayes factor, a common default method for Bayesiancomputation with linear models. In addition to providing computationalexamples, I report a simulation study in which I demonstrate that the formulacompares favorably to a recently developed, more complex method that accountsfor correlation between repeated measurements. The minimal BIC method providesa simple way for researchers to estimate Bayes factors from a minimal set ofsummary statistics, giving users a powerful index for estimating the evidentialvalue of not only their own data, but also the data reported in publishedstudies.

Estimating Bayes factors from minimal summary statistics in repeated measures analysis of variance designs