  In this paper, I develop a formula for estimating Bayes factors directly from
minimal summary statistics produced in repeated measures analysis of variance
designs. The formula, which requires knowing only the $F$-statistic, the number
of subjects, and the number of repeated measurements per subject, is based on
the BIC approximation of the Bayes factor, a common default method for Bayesian
computation with linear models. In addition to providing computational
examples, I report a simulation study in which I demonstrate that the formula
compares favorably to a recently developed, more complex method that accounts
for correlation between repeated measurements. The minimal BIC 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.
