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arXiv · 1905.05569

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

Thomas J. Faulkenberry
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Abstract

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.

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Thomas J. Faulkenberry. 2019-05-14. Estimating Bayes factors from minimal summary statistics in repeated measures analysis of variance designs. https://doi.org/10.51936/abic6583

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