A tutorial on Bayesian analysis of ecological momentary assessment data in psychological research

This tutorial introduces the reader to Bayesian analysis of ecological momentary assessment (EMA) data as applied in psychological sciences. We discuss practical advantages of the Bayesian approach over frequentist methods as well as conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (1) incorporate prior knowledge and beliefs in analyses, (2) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (3) quantify the uncertainty of effect size estimates, and (4) 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, with data and code made 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.

本教程向读者介绍了生态瞬时评估（EMA）数据在心理学科学中的贝叶斯分析。我们讨论了贝叶斯方法相较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学如何帮助EMA研究人员：（1）将先验知识和信念纳入分析，（2）拟合具有多种结果分布的模型，这些分布反映了可能的数据生成过程，（3）量化效应量估计的不确定性，（4）量化对或反对一个信息性假设的证据。我们呈现了一个贝叶斯分析的流程，并提供了基于EMA数据的实例，我们使用（广义）线性混合效应模型分析这些数据，以检验日常自我控制需求是否预测三种不同的酒精后果。所有示例均可重现，数据和代码可在https://osf.io/rh2sw/获取。完成本教程后，读者应能够将贝叶斯工作流程应用于他们自己的EMA数据分析。