Article (Scientific journals)
A robust outlier approach to prevent Type I error inflation in DIF
Magis, David; De Boeck, Paul
2012In Educational and Psychological Measurement, 72, p. 291-311
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Keywords :
Differential item functioning; Mantel-Haenszel; Type I error; Outlier; Robust statistics
Abstract :
[en] The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxys 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, especially in the presence of DIF but also when DIF is absent and in the presence of impact. The purpose of this paper 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 which 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, while the power of the two methods is similar.
Disciplines :
Mathematics
Author, co-author :
Magis, David ;  Université de Liège - ULiège > Département de mathématique > Statistique mathématique
De Boeck, Paul
Language :
English
Title :
A robust outlier approach to prevent Type I error inflation in DIF
Publication date :
2012
Journal title :
Educational and Psychological Measurement
ISSN :
0013-1644
Publisher :
SAGE Publications
Volume :
72
Pages :
291-311
Peer reviewed :
Peer Reviewed verified by ORBi
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since 23 June 2011

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