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Extensions of Mantel-Haenszel for Multilevel DIF Detection

机译：Mantel-Haenszel的扩展，用于多级DIF检测

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摘要

Multilevel data structures are ubiquitous in the assessment of differential item functioning (DIF), particularly in large-scale testing programs. There are a handful of DIF procures for researchers to select from that appropriately account for multilevel data structures. However, little, if any, work has been completed to extend a popular DIF method to this case. Thus, the primary goal of this study was to introduce and investigate the effectiveness of several new options for DIF assessment in the presence of multilevel data with the Mantel-Haenszel (MH) procedure, a popular, flexible, and effective tool for DIF detection. The performance of these new methods was compared with the standard MH technique through a simulation study, where data were simulated in a multilevel framework, corresponding to examinees nested in schools, for example. The standard MH test for DIF detection was employed, along with several multilevel extensions of MH. Results demonstrated that these multilevel tests proved to be preferable to standard MH in a wide variety of cases where multilevel data were present, particularly when the intraclass correlation was relatively large. Implications of this study for practice and future research are discussed.
机译：多级数据结构在评估差异项功能（DIF）时无处不在，尤其是在大型测试程序中。研究人员可以从几个DIF采购文件中选择适当的方式来考虑多级数据结构。但是，将流行的DIF方法扩展到这种情况的工作很少（如果有的话）。因此，本研究的主要目标是使用Mantel-Haenszel（MH）程序（一种流行，灵活且有效的DIF检测工具）在存在多级数据的情况下介绍和研究几种新的DIF评估选项的有效性。通过模拟研究将这些新方法的性能与标准MH技术进行了比较，在模拟研究中，数据是在多层框架中进行模拟的，例如，与嵌套在学校中的应试者相对应。采用了用于DIF检测的标准MH测试，以及MH的多个多级扩展。结果表明，在存在多级数据的各种情况下，尤其是当组内相关性相对较大时，这些多级测试被证明比标准MH更可取。讨论了这项研究对实践和未来研究的意义。

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