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Using Parallel Splits with Self-Report and Other Measures to Enhance Precision in Generalizability Theory Analyses
Journal of Personality Assessment ( IF 3.720 ) Pub Date : 2021-07-29 , DOI: 10.1080/00223891.2021.1938589
Walter P Vispoel 1 , Guanlan Xu 1 , Wei S Schneider 1
Affiliation  

Abstract

Although generalizability theory (G-theory) provides indices of reliability that take multiple sources of measurement error into account, those indices are typically conservative in nature because they reflect random rather than classical parallelism. One way to address these shortcomings is to use parallel splits rather than items as the unit of analysis in G-theory designs. In this article, we provide the most extensive treatment to date in how to effectively integrate parallel splits into an extended set of G-theory designs using data from the newly developed version of the Big Five Inventory (BFI-2; Soto & John). Results revealed that properly designed splits approximated classical parallelism while improving overall score consistency and reducing key components of measurement error. Variance components within appropriately chosen G-theory designs also provided effective means to evaluate the quality of splits and determine the best ways to improve score consistency and reduce specific sources of measurement error. To help readers in applying these techniques, we provide a comprehensive instructional supplement with code in R for creating parallel splits, analyzing all illustrated designs, and modifying those designs for other objectively or subjectively scored measures.



中文翻译:

使用带有自我报告和其他措施的平行拆分来提高泛化性理论分析的准确性

摘要

尽管泛化性理论(G 理论)提供了将多个测量误差源考虑在内的可靠性指标,但这些指标通常在本质上是保守的,因为它们反映的是随机性而不是经典的并行性。解决这些缺点的一种方法是在 G 理论设计中使用平行拆分而不是项目作为分析单位。在本文中,我们提供了迄今为止最广泛的处理方法,说明如何使用来自新开发的 Big Five Inventory (BFI-2; Soto & John) 版本的数据将并行拆分有效地集成到扩展的 G 理论设计集合中。结果表明,正确设计的拆分近似于经典并行性,同时提高了整体分数的一致性并减少了测量误差的关键组成部分。Variance components within appropriately chosen G-theory designs also provided effective means to evaluate the quality of splits and determine the best ways to improve score consistency and reduce specific sources of measurement error. 为了帮助读者应用这些技术,我们提供了一个全面的指导性补充,其中包含 R 代码,用于创建平行拆分、分析所有插图设计以及修改这些设计以用于其他客观或主观评分的测量。

更新日期:2021-07-29
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