Department of Educational Psychology, University of Kansas, Lawrence, Kansas, 66045-3101, USA.
Behav Res Methods. 2018 Dec;50(6):2193-2214. doi: 10.3758/s13428-017-0986-3.
In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.
在许多行为研究领域，多元概化理论（mG 理论）通常用于研究某些多维评估的可靠性。然而，传统的 mG 理论估计——即使用频率方法——存在局限性，导致研究人员无法充分利用 mG 理论在测量可靠性方面提供的信息。相比之下，贝叶斯方法提供的信息比频率方法多。本文介绍了如何在贝叶斯框架中实施 mG 理论分析的指导原则；特别是，提出了 BUGS 代码来拟合 mG 理论中常见的设计，包括单因素设计、双因素交叉设计和双因素嵌套设计。除了与所选设计和相应的 BUGS 代码密切相关的具体示例外，还提供了一个模拟数据集，以演示贝叶斯方法的实用性和优势。本文旨在为进行 mG 理论研究的应用研究人员和方法学家提供一个教程参考。
