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Differential Item Functioning Analysis Without A Priori Information on Anchor Items: QQ Plots and Graphical Test
Psychometrika ( IF 3 ) Pub Date : 2021-03-03 , DOI: 10.1007/s11336-021-09746-5
Ke-Hai Yuan 1 , Hongyun Liu 2 , Yuting Han 2
Affiliation  

Differential item functioning (DIF) analysis is an important step in establishing the validity of measurements. Most traditional methods for DIF analysis use an item-by-item strategy via anchor items that are assumed DIF-free. If anchor items are flawed, these methods will yield misleading results due to biased scales. In this article, based on the fact that the item’s relative change of difficulty difference (RCD) does not depend on the mean ability of individual groups, a new DIF detection method (RCD-DIF) is proposed by comparing the observed differences against those with simulated data that are known DIF-free. The RCD-DIF method consists of a D-QQ (quantile quantile) plot that permits the identification of internal references points (similar to anchor items), a RCD-QQ plot that facilitates visual examination of DIF, and a RCD graphical test that synchronizes DIF analysis at the test level with that at the item level via confidence intervals on individual items. The RCD procedure visually reveals the overall pattern of DIF in the test and the size of DIF for each item and is expected to work properly even when the majority of the items possess DIF and the DIF pattern is unbalanced. Results of two simulation studies indicate that the RCD graphical test has Type I error rate comparable to those of existing methods but with greater power.



中文翻译:

无锚项先验信息的微分项功能分析:QQ图和图形检验

差异项目功能 (DIF) 分析是建立测量有效性的重要步骤。大多数传统的 DIF 分析方法通过假设无 DIF 的锚项使用逐项策略。如果锚项有缺陷,这些方法将由于有偏差的尺度而产生误导性的结果。在本文中,基于项目的难度差异(RCD)的相对变化不依赖于个体组的平均能力的事实,通过将观察到的差异与具有差异的差异进行比较,提出了一种新的DIF检测方法(RCD-DIF)已知无 DIF 的模拟数据。RCD-DIF 方法包括允许识别内部参考点(类似于锚点)的 D-QQ(分位数分位数)图,便于 DIF 目视检查的 RCD-QQ 图,和 RCD 图形测试,通过单个项目的置信区间将测试级别的 DIF 分析与项目级别的 DIF 分析同步。RCD 程序直观地揭示了测试中 DIF 的整体模式和每个项目的 DIF 大小,即使大多数项目具有 DIF 且 DIF 模式不平衡,也有望正常工作。两项模拟研究的结果表明,RCD 图形测试具有与现有方法相当的 I 类错误率,但具有更大的功效。RCD 程序直观地揭示了测试中 DIF 的整体模式和每个项目的 DIF 大小,即使大多数项目具有 DIF 且 DIF 模式不平衡,也有望正常工作。两项模拟研究的结果表明,RCD 图形测试具有与现有方法相当的 I 类错误率,但具有更大的功效。RCD 程序直观地揭示了测试中 DIF 的整体模式和每个项目的 DIF 大小,即使大多数项目具有 DIF 且 DIF 模式不平衡,也有望正常工作。两项模拟研究的结果表明,RCD 图形测试具有与现有方法相当的 I 类错误率,但具有更大的功效。

更新日期:2021-03-03
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