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Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data
Version 1
: Received: 9 July 2020 / Approved: 12 July 2020 / Online: 12 July 2020 (16:59:18 CEST)
A peer-reviewed article of this Preprint also exists.
Robitzsch, A. Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data. J. Intell. 2020, 8, 30. Robitzsch, A. Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data. J. Intell. 2020, 8, 30.
Abstract
The last series of Raven's standard progressive matrices (SPM-LS) test were 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 (RLCM). For dichotomous item response data, an alternative estimation approach for RLCMs is proposed. For polytomous item responses, different alternatives for performing regularized latent class analysis are proposed. 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.
Keywords
regularized latent class analysis; regularization; fused regularization; fused grouped regularization; distractor analysis
Subject
Computer Science and Mathematics, Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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