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Regularized latent class analysis for polytomous item responses : An application to SPM-LS data. / Robitzsch, Alexander.

In: Journal of Intelligence, Vol. 8, No. 3, 30, 14.08.2020.

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@article{cb5600eeb4e345a8a4dee4a5e249a257,
title = "Regularized latent class analysis for polytomous item responses: An application to SPM-LS data",
abstract = "The last series of Raven{\textquoteright}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.",
keywords = "Methodological research and development, regularized latent class analysis, regularization, fused regularization, fused grouped regularization, distractor analysis",
author = "Alexander Robitzsch",
year = "2020",
month = aug,
day = "14",
doi = "10.3390/jintelligence8030030",
language = "English",
volume = "8",
journal = "Journal of Intelligence",
issn = "2079-3200",
publisher = "MDPI",
number = "3",

}

RIS

TY - JOUR

T1 - Regularized latent class analysis for polytomous item responses

T2 - An application to SPM-LS data

AU - Robitzsch, Alexander

PY - 2020/8/14

Y1 - 2020/8/14

N2 - 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.

AB - 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.

KW - Methodological research and development

KW - regularized latent class analysis

KW - regularization

KW - fused regularization

KW - fused grouped regularization

KW - distractor analysis

U2 - 10.3390/jintelligence8030030

DO - 10.3390/jintelligence8030030

M3 - Journal article

VL - 8

JO - Journal of Intelligence

JF - Journal of Intelligence

SN - 2079-3200

IS - 3

M1 - 30

ER -

ID: 1409811