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A Penalty Approach to Differential Item Functioning in Rasch Models

Published online by Cambridge University Press:  01 January 2025

Gerhard Tutz  and
Gunther Schauberger
Gerhard Tutz*
Affiliation:
Ludwig-Maximilians-Universität
Gunther Schauberger
Affiliation:
Ludwig-Maximilians-Universität
*
Requests for reprints should be sent to Gerhard Tutz, Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany. E-mail: gerhard.tutz@stat.uni-muenchen.de
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Abstract

A new diagnostic tool for the identification of differential item functioning (DIF) is proposed. Classical approaches to DIF allow to consider only few subpopulations like ethnic groups when investigating if the solution of items depends on the membership to a subpopulation. We propose an explicit model for differential item functioning that includes a set of variables, containing metric as well as categorical components, as potential candidates for inducing DIF. The ability to include a set of covariates entails that the model contains a large number of parameters. Regularized estimators, in particular penalized maximum likelihood estimators, are used to solve the estimation problem and to identify the items that induce DIF. It is shown that the method is able to detect items with DIF. Simulations and two applications demonstrate the applicability of the method.

Keywords

Rasch modeldifferential item functioningpenalized maximum likelihoodDIF lasso

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 1 , March 2015 , pp. 21 - 43
Copyright
Copyright © 2013 The Psychometric Society

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