用递归分区检测多维分级反应模型中的差异项目功能

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Franz Classe, Christoph Kern
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引用次数: 0

摘要

在大规模调查中研究潜在特质时,项目功能差异(DIF)是一个常见的挑战。在最近的工作中,有人提出了机器学习领域的方法,如基于模型的递归分区法,用于在缺乏理论指导和存在许多潜在子组的情况下识别具有 DIF 的子组。在此基础上,我们提出并比较了用于检测 DIF 的递归分区技术,重点是具有多个潜变量和顺序响应数据的测量模型。我们采用基于树的方法来识别在多维潜变量建模中导致 DIF 的子群,并受随机森林的启发提出了一种稳健且可扩展的扩展方法。我们应用了所提出的技术并进行了模拟比较。我们发现,所提出的方法能够有效地检测 DIF,并能提取出决策规则,从而产生具有良好拟合模型的子群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning
Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.
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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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