Using Logistic Regression and the Mantel‐Haenszel With Multiple Ability Estimates to Detect Differential Item Functioning

Kathleen M. Mazor · Anil Kanjee · Brian E. Clauser

53 citations0/0 files storedJournal of Educational Measurement

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Psychometric Methodologies and Testing Educational Assessment and Pedagogy Educational Technology and Assessment

Abstract

Logistic regression has recently been advanced as a viable procedure for detecting differential item functioning (DIF). One of the advantages of this procedure is the considerable flexibility it offers in the specification of the regression equation. This article describes incorporating two ability estimates into a single regression analysis, with the result that substantially fewer items exhibit DIF. A comparable analysis is conducted using the Mantel‐Haenszel with similar results. It is argued that by simultaneously conditioning on two relevant ability estimates, more accurate matching of examinees in the reference and focal groups is obtained, and thus multidimensional item impact is not mistakenly identified as DIF.

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References

1994Differential Item Functioning.1990Detecting Differential Item Functioning Using Logistic Regression Procedures 2013Differential Item Performance and the Mantel–Haenszel Procedure 1993A Model-Based Standardization Approach that Separates True Bias/DIF from Group Ability Differences and Detects Test Bias/DTF as well as Item Bias/DIF 1992DIF DETECTION AND DESCRIPTION: MANTEL‐HAENSZEL AND STANDARDIZATION1,2 1985The Difficulty of Test Items That Measure More Than One Ability 1992A Didactic Explanation of Item Bias, Item Impact, and Item Validity From a Multidimensional Perspective 1993Perspectives on differential item functioning methodology.

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2000Racial and gender bias in ability and achievement tests: Resolving the apparent paradox.1998Multidimensional DIF Analyses: The Effects of Matching on Unidimensional Subtest Scores 1996Improving the Matching for DIF Analysis by Conditioning on Both Test Score and an Educational Background Variable

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