a Department of Psychological Sciences , University of California, Merced , Merced , CA , USA.
b Department of Methods and Statistics , Utrecht University , Utrecht , The Netherlands.
Health Psychol Rev. 2017 Sep;11(3):248-264. doi: 10.1080/17437199.2017.1343676. Epub 2017 Jul 11.
The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.
本文旨在向健康心理学领域的专业人士简要介绍贝叶斯统计学。贝叶斯方法在应用领域中越来越流行，模拟研究表明，它们可以提高结构方程模型、潜在增长曲线（和混合）模型以及层次线性模型的估计准确性。同样，由于贝叶斯方法不依赖于大样本理论，因此可以用于小样本量。在本文中，我们将讨论贝叶斯统计学中与基于健康的研究相关的几个重要组成部分。我们讨论了将先验知识纳入估计过程的方法以及在文章中应报告的分析的不同组成部分。我们通过一个在参与者经历急性压力源后血压变化的背景下实施贝叶斯估计的示例来说明问题。最后，我们对健康心理学中贝叶斯统计的实施提出了一些想法，包括审查贝叶斯论文和资助提案的建议。我们还提供了大量在线补充材料，以补充这里介绍的内容，包括使用许多不同软件程序的贝叶斯示例以及广泛的敏感性分析，以检查先验的影响。
