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.

中文翻译：

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使用递归分区检测多维分级响应模型中的差异项功能

在大规模调查中检查潜在特征时，差异项目功能 (DIF) 是一个常见的挑战。在最近的工作中，当理论指导很少且有许多潜在子组可用时，机器学习领域的方法（例如基于模型的递归划分）被提出来识别具有 DIF 的子组。在此基础上，我们提出并比较了用于检测 DIF 的递归划分技术，重点关注具有多个潜在变量和序数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有助于 DIF 的子组，并受随机森林的启发，提出了一种稳健且可扩展的扩展。应用所提出的技术并与模拟进行比较。我们表明，所提出的方法能够有效地检测 DIF 并允许提取决策规则，从而产生具有良好拟合模型的子组。