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A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning

机译：防止差异项功能中的I型错误膨胀的鲁棒离群方法

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摘要

The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source of potential Type I error inflation, not only in the presence of DIF but also when DIF is absent and in the presence of impact. The purpose of this article is to present an alternative statistical inference approach based on the same measure of DIF but such that the Type I error inflation is prevented. The key notion is that for DIF items, the measure has an outlying value that can be identified as such with inference tools from robust statistics. Although we use the MH log odds ratio as a statistic, the inference is different. A simulation study is performed to compare the robust statistical inference with the classical inference method, both based on the MH statistic. As expected, the Type I error rate inflation is avoided with the robust approach, although the power of the two methods is similar.
机译：差异项功能（DIF）的识别通常是通过将原始分数视为能力特征水平的代理的统计方法来进行的。最受欢迎的方法之一是Mantel-Haenszel（MH）方法，属于此类。但是，用简单的原始分数代替能力级别是潜在的I型错误膨胀的来源，不仅在存在DIF的情况下，而且在不存在DIF的情况下和在存在影响的情况下也是如此。本文的目的是提供一种基于相同DIF度量的替代统计推断方法，但可以防止I型错误膨胀。关键概念是，对于DIF项目，该度量值具有一个异常值，可以使用可靠统计数据中的推断工具将其识别出来。尽管我们将MH对数比值比率用作统计数据，但推论是不同的。进行了仿真研究，以将鲁棒的统计推断与经典推断方法进行比较，二者均基于MH统计量。正如预期的那样，尽管两种方法的功能相似，但通过稳健的方法可以避免I型错误率膨胀。

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