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

• Published in: Metodološki zvezki (Spletna izd.) Vol. 17; no. 1; pp. 1 - 17
• Main Author: Faulkenberry, Thomas
• Format: Journal Article
• Language: English
• Published: Ljubljana Anuska Ferligoj 01.01.2020
• Subjects: Within-subjects design
• ISSN: 1854-0023; 1854-0031
• DOI: 10.51936/abic6583

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.