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Item Bias Detection using Loglinear IRT

Published online by Cambridge University Press:  01 January 2025

Henk Kelderman
Henk Kelderman*
Affiliation:
University of Twente
*
Requests for reprints should be sent to Henk Kelderman, University of Twente, PO Box 217, 7500 AE Enschede, THE NETHERLANDS.
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Abstract

A method is proposed for the detection of item bias with respect to observed or unobserved subgroups. The method uses quasi-loglinear models for the incomplete subgroup × test score × Item 1 × ... × item k contingency table. If subgroup membership is unknown the models are Haberman's incomplete-latent-class models.

The (conditional) Rasch model is formulated as a quasi-loglinear model. The parameters in this loglinear model, that correspond to the main effects of the item responses, are the conditional estimates of the parameters in the Rasch model. Item bias can then be tested by comparing the quasi-loglinear-Rasch model with models that contain parameters for the interaction of item responses and the subgroups.

Keywords

loglinear modelsRasch modelitem biasdifferential item performancelatentclass modelsIRT

Information

Type
Original Paper
Information
Psychometrika , Volume 54 , Issue 4 , December 1989, pp. 681 - 697
Copyright
Copyright © 1989 The Psychometric Society

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Footnotes

The author thanks Wim J. van der Linden and Gideon J. Mellenbergh for comments and suggestions and Frank Kok for empirical data.

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