Department of Psychological Sciences, University of Missouri.
School of Psychology, Cardiff University.
Psychol Methods. 2017 Jun;22(2):304-321. doi: 10.1037/met0000057. Epub 2016 Jun 9.
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
本文提供了一种贝叶斯因子方法，用于多维方差分析 (ANOVA)，使研究人员能够根据数据对效应或不变性进行分级证据陈述。ANOVA 被概念化为一个层次模型，其中水平在因素内聚类。该发展是全面的，它包括固定和随机效应的贝叶斯因子，以及被试内、被试间和混合设计的贝叶斯因子。讨论了不同的模型构建和比较策略，并提供了一个示例。我们展示了如何使用 R 中的 BayesFactor 包和 JASP 统计软件包计算贝叶斯因子。
