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A Mokken Scale Analysis of the Last Series of the Standard Progressive Matrices (SPM-LS)
Article

Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

1
IPN—Leibniz Institute for Science and Mathematics Education, D-24098 Kiel, Germany
2
Centre for International Student Assessment (ZIB), D-24098 Kiel, Germany
Received: 9 July 2020 / Revised: 26 July 2020 / Accepted: 10 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Analysis of an Intelligence Dataset)
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. View Full-Text
Keywords: regularized latent class analysis; regularization; fused regularization; fused grouped regularization; distractor analysis regularized latent class analysis; regularization; fused regularization; fused grouped regularization; distractor analysis
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MDPI and ACS Style

Robitzsch, A. Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data. J. Intell. 2020, 8, 30. https://doi.org/10.3390/jintelligence8030030

AMA Style

Robitzsch A. Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data. Journal of Intelligence. 2020; 8(3):30. https://doi.org/10.3390/jintelligence8030030

Chicago/Turabian Style

Robitzsch, Alexander. 2020. "Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data" Journal of Intelligence 8, no. 3: 30. https://doi.org/10.3390/jintelligence8030030

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