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Bayesian Adaptive Lasso for Detecting Item–Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models

Published online by Cambridge University Press:  01 January 2025

Na ShanOpen the ORCID record for Na Shan[Opens in a new window] and
Ping-Feng Xu
Na Shan*
Affiliation:
Northeast Normal University
Ping-Feng Xu
Affiliation:
Northeast Normal UniversityShanghai Zhangjiang Institute of Mathematics
*
Correspondence should bemade to Na Shan, School of Psychology&Key Laboratory of Applied Statistics of MOE, Northeast Normal University, 5268 Renmin Street, Changchun, Jilin, China. Email: shanna1981@126.com
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Abstract

In multidimensional tests, the identification of latent traits measured by each item is crucial. In addition to item–trait relationship, differential item functioning (DIF) is routinely evaluated to ensure valid comparison among different groups. The two problems are investigated separately in the literature. This paper uses a unified framework for detecting item–trait relationship and DIF in multidimensional item response theory (MIRT) models. By incorporating DIF effects in MIRT models, these problems can be considered as variable selection for latent/observed variables and their interactions. A Bayesian adaptive Lasso procedure is developed for variable selection, in which item–trait relationship and DIF effects can be obtained simultaneously. Simulation studies show the performance of our method for parameter estimation, the recovery of item–trait relationship and the detection of DIF effects. An application is presented using data from the Eysenck Personality Questionnaire.

Keywords

Bayesian adaptive Lassoitem–trait relationshipdifferential item functioningmultidimensional item response theory modelregularization

Information

Type
Original Research
Information
Psychometrika , Volume 89 , Issue 4 , December 2024, pp. 1337 - 1365
Copyright
© 2024 The Author(s), under exclusive licence to The Psychometric Society

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