Alter Udi, Too Miranda A, Cribbie Robert A
Department of Psychology, York University, Toronto, ON, Canada.
Int J Psychol. 2025 Feb;60(1):e13271. doi: 10.1002/ijop.13271.
Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology researchers and introduces Bayesian analysis as an accessible and powerful tool. We begin by comparing Bayesian and frequentist approaches, redefining fundamental terms from both perspectives with practical illustrations. Our exploration of Bayesian statistics includes Bayes's Theorem, likelihood, prior and posterior distributions, various prior types, and Markov-Chain Monte Carlo (MCMC) methods, supplemented by graphical aids for clarity. To bridge theory and practice, we employ a psychological research example with real, open data. We analyse the data using both frequentist and Bayesian approaches, providing R code and comprehensive supporting information, and emphasising best practices for interpretation and reporting. We discuss and demonstrate how to interpret parameter estimates and credible intervals, among other essential topics. Throughout, we maintain an accessible and user-friendly language, focusing on practical implications, intuitive examples, and actionable recommendations.
贝叶斯统计在社会科学领域，尤其是心理学中已获得了广泛的认可。尽管它在心理学文献中的地位日益突出，但许多研究人员仍不熟悉贝叶斯方法及其优势。本教程旨在满足好奇的应用心理学研究人员的需求，将贝叶斯分析作为一种易于理解且强大的工具进行介绍。我们首先比较贝叶斯方法和频率学派方法，通过实际示例从两个角度重新定义基本术语。我们对贝叶斯统计的探索包括贝叶斯定理、似然性、先验分布和后验分布、各种先验类型以及马尔可夫链蒙特卡罗（MCMC）方法，并辅以图表以增强清晰度。为了将理论与实践相结合，我们采用了一个具有真实开放数据的心理学研究示例。我们使用频率学派和贝叶斯方法对数据进行分析，提供R代码和全面的支持信息，并强调解释和报告的最佳实践。我们讨论并展示如何解释参数估计和可信区间等其他重要主题。在整个教程中，我们使用通俗易懂且用户友好的语言，重点关注实际应用、直观示例和可行的建议。
