The last series of Raven’s standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.
Original languageEnglish
Article number30
JournalJournal of Intelligence
Issue number3
Number of pages24
Publication statusPublished - 14.08.2020
No renderer: handleNetPortal,dk.atira.pure.api.shared.model.researchoutput.ContributionToJournal

    Research areas

  • regularized latent class analysis, regularization, fused regularization, fused grouped regularization, distractor analysis

ID: 1409811