Multivariate generalizability theory (GT) represents a comprehensive framework for quantifying score consistency, separating multiple sources contributing to measurement error, correcting correlation coefficients for such error, assessing subscale viability, and determining the best ways to change measurement procedures at different levels of score aggregation. Despite such desirable attributes, multivariate GT has rarely been applied when measuring psychological constructs and far less often than univariate techniques that are subsumed within that framework. Our purpose in this tutorial is to describe multivariate GT in a simple way and illustrate how it expands and complements univariate procedures. We begin with a review of univariate GT designs and illustrate how such designs serve as subcomponents of corresponding multivariate designs. Our empirical examples focus primarily on subscale and composite scores for objectively scored measures, but guidelines are provided for applying the same techniques to subjectively scored performance and clinical assessments. We also compare multivariate GT indices of score consistency and measurement error to those obtained using alternative GT-based procedures and across different software packages for analyzing multivariate GT designs. Our online supplemental materials include instruction, code, and output for common multivariate GT designs analyzed using mGENOVA and the gtheory, glmmTMB, lavaan, and related packages in R. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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将多元普遍性理论应用于心理评估。


多元概括性理论（GT）代表了一个综合框架，用于量化分数一致性、分离导致测量误差的多个来源、校正此类误差的相关系数、评估子量表的可行性以及确定在不同分数聚合级别改变测量程序的最佳方法。尽管有这些令人向往的属性，但在测量心理结构时，多变量 GT 很少被应用，而且远低于该框架内包含的单变量技术。我们在本教程中的目的是以简单的方式描述多变量 GT 并说明它如何扩展和补充单变量过程。我们首先回顾单变量 GT 设计，并说明此类设计如何作为相应多变量设计的子组件。我们的实证示例主要关注客观评分测量的子量表和综合评分，但也提供了将相同技术应用于主观评分表现和临床评估的指南。我们还将分数一致性和测量误差的多元 GT 指数与使用基于 GT 的替代程序和不同软件包获得的指数进行比较，以分析多元 GT 设计。我们的在线补充材料包括使用 mGENOVA 和 gtheory、glmmTMB、lavaan 以及 R 中的相关包分析的常见多变量 GT 设计的指令、代码和输出。（PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。