Berkeley School of Education, University of California Berkeley, Berkeley, CA, USA.
Teachers College, Columbia University, New York, NY, USA.
Behav Res Methods. 2024 Mar;56(3):1817-1837. doi: 10.3758/s13428-023-02121-5. Epub 2023 Apr 24.
IRTree models have been receiving increasing attention. However, to date, there are limited sources that provide a systematic introduction to Bayesian modeling techniques using modern probabilistic programming frameworks for the implementation of IRTree models. To facilitate the research and application of IRTree models, this paper introduces how to perform two families of Bayesian IRTree models (i.e., response tree models and latent tree models) in Stan and how to extend them in an explanatory way. Some suggestions on executing Stan codes and checking convergence are also provided. An empirical study based on the Oxford Achieving Resilience during COVID-19 data was conducted as an example to further illustrate how to apply Bayesian IRTree models to address research questions. Finally, strengths and future directions are discussed.
IRTree 模型越来越受到关注。然而，到目前为止，很少有资源系统地介绍如何使用现代概率编程框架为 IRTree 模型实现贝叶斯建模技术。为了促进 IRTree 模型的研究和应用，本文介绍了如何在 Stan 中执行两类贝叶斯 IRTree 模型（即反应树模型和潜在树模型），以及如何以解释的方式对其进行扩展。还提供了一些关于执行 Stan 代码和检查收敛性的建议。基于牛津大学在 COVID-19 期间实现韧性的数据进行了实证研究，进一步说明了如何应用贝叶斯 IRTree 模型来解决研究问题。最后，讨论了优势和未来方向。
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