Item bias detection using loglinear IRT

Henk Kelderman

Research output: Contribution to journalArticleAcademic

40 Citations (Scopus)
413 Downloads (Pure)


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 × ... × itemk 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.
Original languageUndefined
Pages (from-to)681-697
Issue number4
Publication statusPublished - 1989


  • differential item performance
  • latentclass models
  • IRT
  • IR-85752
  • Item Bias
  • Rasch model
  • Loglinear models

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