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

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Title: A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning
Language: English
Authors: Magis, David, De Boeck, Paul
Source: Educational and Psychological Measurement. Apr 2012 72(2):291-311.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Peer Reviewed: Y
Page Count: 21
Publication Date: 2012
Document Type: Journal Articles
Reports - Research
Descriptors: Test Bias, Data, Error of Measurement, Raw Scores, Statistical Inference, Robustness (Statistics), Simulation, Statistical Analysis
DOI: 10.1177/0013164411416975
ISSN: 0013-1644
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, 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. (Contains 6 figures.)
Abstractor: As Provided
Number of References: 50
Entry Date: 2012
Accession Number: EJ960249
Database: ERIC
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  Data: 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. (Contains 6 figures.)
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