Dora Jonas, McCabe Connor J, van Lissa Caspar J, Witkiewitz Katie, King Kevin M
Department of Psychology, University of Washington, Seattle, Washington.
Department of Methodology & Statistics, Tilburg University, Tilberg, the Netherlands.
Adv Methods Pract Psychol Sci. 2024 Jan-Mar;7(1). doi: 10.1177/25152459241235875. Epub 2024 Mar 28.
In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/. Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.
在本教程中，我们向读者介绍如何使用贝叶斯（广义）线性混合效应模型来分析应用于心理科学领域的生态瞬时评估（EMA）数据。我们讨论了贝叶斯方法相对于频率主义方法的实际优势以及概念上的差异。我们展示了贝叶斯统计如何帮助EMA研究人员：（a）在分析中纳入先验知识和信念；（b）拟合具有各种反映可能数据生成过程的结果分布的模型；（c）量化效应大小估计的不确定性；以及（d）量化支持或反对信息性假设的证据。我们展示了贝叶斯分析的工作流程，并基于EMA数据提供了示例，我们使用（广义）线性混合效应模型来分析这些数据，以检验每日自我控制需求是否能预测三种不同的酒精相关结果。所有示例都是可重现的，数据和代码可在https://osf.io/rh2sw/获取。通过学习本教程，读者应该能够采用贝叶斯工作流程来进行自己的EMA数据分析。
