An Introduction to Missing Data in the Context of Differential Item Functioning.

Q2 Social Sciences
Kathleen P Banks
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引用次数: 11

Abstract

This article introduces practitioners and researchers to the topic of missing data in the context of differential item functioning (DIF), reviews the current literature on the issue, discusses implications of the review, and offers suggestions for future research. A total of nine studies were reviewed. All of these studies determined what effect particular missing data techniques would have on the results of certain DIF detection procedures under various conditions. The most important finding of this review involved the use of zero imputation as a missing data technique. The review shows that zero imputation can lead to inflated Type I errors, especially in cases where the examinees ability level has not been taken into consideration.
差异项目功能背景下缺失数据的介绍。
本文介绍了从业者和研究人员对差异项目功能(DIF)背景下缺失数据的主题,回顾了目前关于这一问题的文献,讨论了综述的意义,并提出了对未来研究的建议。共回顾了9项研究。所有这些研究都确定了在各种条件下,特定缺失数据技术对某些DIF检测程序的结果会产生什么影响。本综述最重要的发现包括使用零输入作为缺失数据技术。审查表明,零归因会导致I类错误膨胀,特别是在没有考虑考生能力水平的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.60
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0.00%
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