Department of Psychology, Sun Yat-sen University, Guangzhou, P. R. China.
Graduate School of Education, Stanford University, Stanford, CA, USA.
Int J Psychol. 2024 Dec;59(6):1326-1346. doi: 10.1002/ijop.13258. Epub 2024 Oct 10.
This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.
本文探讨了贝叶斯结构方程模型（BSEM）在心理学中的应用，强调了其在处理复杂模型和小样本量方面相对于频率派方法的优势。介绍了 BSEM 的基本概念和基本问题，如先验设定、模型收敛和模型拟合评估等。本文还提供了常用的 BSEM 的实例，包括验证性因子分析（CFA）模型、中介模型和多群组 CFA 模型，并附有实证数据和计算机代码，以方便实施。我们的目标是为研究人员提供新的实证研究思路，使他们能够克服传统方法固有的挑战。随着 BSEM 在各个领域的不断发展，我们预计其发展将具有改进的方法、技术和报告标准。
