The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders the interpretability of model parameters. This article proposes regularized estimation of the mixture Rasch model that imposes some sparsity structure on class-specific item difficulties. We illustrate the feasibility of the proposed modeling approach by means of one simulation study and two simulated case studies.
Original languageEnglish
Article number534
Issue number11
Publication statusPublished - 10.11.2022
No renderer: handleNetPortal,dk.atira.pure.api.shared.model.researchoutput.ContributionToJournal

    Research areas

  • Methodological research and method development - mixture Rasch model, regularization, penalty, differential item functioning

ID: 5680893