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.

翻译：在本文中,我从对差异设计进行反复计量分析后产生的最低限度简要统计中直接得出估计贝叶斯系数的公式。该公式仅要求了解美元-统计、科目数目和每科目重复测量的次数,其依据是BIC Bayes系数的近似值,这是巴伊西亚计算时使用线性模型的一种常见默认方法。除了提供计算实例外,我还报告了一项模拟研究,其中我表明该公式比最近开发的、更复杂的方法更为有利,该方法说明了重复测量之间的相互关系。 最低限度的BIC方法为研究人员从最低限度的一组简要统计中估算贝伊斯系数提供了一个简单的方法,使用户有一个强大的指数,用以估计不仅自己的数据的证据价值,而且还包括已发表的研究中报告的数据。