Title: A robust outlier approach to prevent Type I error inflation in Differential Item Functioning
Authors: Magis, David ×
De Boeck, Paul #
Issue Date: 2012
Series Title: Educational and Psychological Measurement vol:72 issue:2 pages:291-311
Abstract: 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, be-
longs 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.
ISSN: 0013-1644
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
× corresponding author
# (joint) last author

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