通过稳健标度进行差异项目功能分析
Differential item functioning via robust scaling
AI总结：
本文将 IRT 标度中的 DIF 评估转化为异常值检测问题，提出无需预设锚题的回降 M 估计方法，并通过理论、模拟和真实数据验证其有效性。
摘要：
本文提出了一种在项目反应理论（IRT）模型中评估差异项目功能（DIF）的方法。该方法不需要预先指定锚题，这是其主要优点。它通过两个主要步骤展开：首先说明如何将 DIF 重新表述为基于 IRT 的标度中的异常值检测问题，然后利用稳健统计学方法处理该问题。所提出的方法是一种用于 IRT 标度参数的回降 M 估计量，经过调谐后可按期望的渐近第一类错误率标记存在 DIF 的项目。理论结果描述了该估计量在不存在 DIF 时的效率，以及在存在 DIF 时的稳健性。模拟研究表明，与现有 DIF 检测方法相比，所提方法表现良好；一个真实数据示例展示了其在无法预先指定锚题的研究情境中的应用。本文重点关注两个独立组中的双参数 logistic 模型，并在结论中考虑了向其他情境的扩展。
英文摘要：
This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps, first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling, then tackling the latter using methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic Type I Error rate. Theoretical results describe the efficiency of the estimator in the absence of DIF and its robustness in the presence of DIF. Simulation studies show that the proposed method compares favorably to currently available approaches for DIF detection, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
