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Anchor Selection Strategies for DIF Analysis

机译:DIF分析的锚点选择策略

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摘要

Differential item functioning (DIF) indicates the violation of the invariance assumption, for instance, in models based on item response theory (IRT). For item-wise DIF analysis using IRT, a common metric for the item parameters of the groups that are to be compared (e.g., for the reference and the focal group) is necessary. In the Rasch model, therefore, the same linear restriction is imposed in both groups. Items in the restriction are termed the ``anchor items''. Ideally, these items are DIF-free to avoid artificially augmented false alarm rates. However, the question how DIF-free anchor items are selected appropriately is still a major challenge. Furthermore, various authors point out the lack of new anchor selection strategies and the lack of a comprehensive study especially for dichotomous IRT models. This article reviews existing anchor selection strategies that do not require any knowledge prior to DIF analysis, offers a straightforward notation, and proposes three new anchor selection strategies. An extensive simulation study is conducted to compare the performance of the anchor selection strategies. The results show that an appropriate anchor selection is crucial for suitable item-wise DIF analysis. The newly suggested anchor selection strategies outperform the existing strategies and can reliably locate a suitable anchor when the sample sizes are large enough.
机译:差异项功能(DIF)表示违反了不变性假设,例如,在基于项响应理论(IRT)的模型中。对于使用IRT进行逐项DIF分析,需要针对要比较的组(例如,对于参考组和焦点组)的项参数使用通用度量。因此,在Rasch模型中,两组都施加相同的线性限制。限制中的项目称为``锚项目''。理想情况下,这些项目均不含DIF,以避免人为增加的误报率。但是,如何正确选择不含DIF的锚点的问题仍然是一个重大挑战。此外,各种作者指出,缺乏新的锚点选择策略,也缺乏针对二分式IRT模型的全面研究。本文回顾了在DIF分析之前不需要任何知识的现有锚选择策略,提供了一个简单的表示法,并提出了三种新的锚选择策略。进行了广泛的仿真研究,以比较锚点选择策略的性能。结果表明,适当的锚点选择对于适当的逐项DIF分析至关重要。新提出的锚点选择策略优于现有策略,并且在样本量足够大时可以可靠地定位合适的锚点。

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