Bayesian analysis of factorial designs

This article provides a Bayes factor approach to multiway analysis of variance (ANOVA) that allows researchers to state graded evidence for effects or invariances as determined by the data. ANOVA is conceptualized as a hierarchical model where levels are clustered within factors. The development is...

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Bibliographic Details
Published inPsychological methods Vol. 22; no. 2; p. 304
Main Authors Rouder, Jeffrey N, Morey, Richard D, Verhagen, Josine, Swagman, April R, Wagenmakers, Eric-Jan
Format Journal Article
LanguageEnglish
Published United States 01.06.2017
Subjects
Analysis of Variance
Bayes Theorem
Humans
Models, Statistical
Research Design
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Summary:This article provides a Bayes factor approach to multiway analysis of variance (ANOVA) that allows researchers to state graded evidence for effects or invariances as determined by the data. ANOVA is conceptualized as a hierarchical model where levels are clustered within factors. The development is comprehensive in that it includes Bayes factors for fixed and random effects and for within-subjects, between-subjects, and mixed designs. Different model construction and comparison strategies are discussed, and an example is provided. We show how Bayes factors may be computed with BayesFactor package in R and with the JASP statistical package. (PsycINFO Database Record
ISSN:1939-1463
DOI:10.1037/met0000057