Estimating Bayes factors from minimal summary statistics in repeated measures analysis of variance designs
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,...
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| Vydáno v: | Metodološki zvezki (Spletna izd.) Ročník 17; číslo 1; s. 1 - 17 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Ljubljana
Anuska Ferligoj
01.01.2020
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| Témata: | |
| ISSN: | 1854-0023, 1854-0031 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| Bibliografie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| ISSN: | 1854-0023 1854-0031 |
| DOI: | 10.51936/abic6583 |