Department of Methodology and Statistics.
Department of Educational Psychology.
Psychol Methods. 2019 Oct;24(5):539-556. doi: 10.1037/met0000201. Epub 2019 Feb 11.
Learning about hypothesis evaluation using the Bayes factor could enhance psychological research. In contrast to null-hypothesis significance testing it renders the evidence in favor of each of the hypotheses under consideration (it can be used to quantify support for the null-hypothesis) instead of a dichotomous reject/do-not-reject decision; it can straightforwardly be used for the evaluation of multiple hypotheses without having to bother about the proper manner to account for multiple testing; and it allows continuous reevaluation of hypotheses after additional data have been collected (Bayesian updating). This tutorial addresses researchers considering to evaluate their hypotheses by means of the Bayes factor. The focus is completely applied and each topic discussed is illustrated using Bayes factors for the evaluation of hypotheses in the context of an ANOVA model, obtained using the R package bain. Readers can execute all the analyses presented while reading this tutorial if they download bain and the R-codes used. It will be elaborated in a completely nontechnical manner: what the Bayes factor is, how it can be obtained, how Bayes factors should be interpreted, and what can be done with Bayes factors. After reading this tutorial and executing the associated code, researchers will be able to use their own data for the evaluation of hypotheses by means of the Bayes factor, not only in the context of ANOVA models, but also in the context of other statistical models. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
使用贝叶斯因子了解假设评估可以增强心理学研究。与零假设显著性检验不同，它呈现了考虑中的每个假设的有利证据（可用于量化对零假设的支持），而不是二分的拒绝/不拒绝决策；它可以直接用于评估多个假设，而不必担心正确的多测试处理方式；并且它允许在收集更多数据后对假设进行连续重新评估（贝叶斯更新）。本教程针对考虑通过贝叶斯因子评估其假设的研究人员。重点完全是应用的，讨论的每个主题都使用贝叶斯因子在 ANOVA 模型的上下文中进行说明，使用 R 包 bain 获得。如果读者下载 bain 和使用的 R 代码，他们可以在阅读本教程的同时执行所有呈现的分析。它将以完全非技术性的方式阐述：贝叶斯因子是什么，如何获得，如何解释贝叶斯因子，以及可以用贝叶斯因子做什么。阅读本教程并执行相关代码后，研究人员将能够使用自己的数据通过贝叶斯因子评估假设，不仅在 ANOVA 模型的上下文中，而且在其他统计模型的上下文中。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
