Bi-factor and second-order copula models for item response data

Kadhem, Sayed H. and Nikoloulopoulos, Aristidis K. ORCID: https://orcid.org/0000-0003-0853-0084 (2023) Bi-factor and second-order copula models for item response data. Psychometrika, 88. pp. 132-157. ISSN 0033-3123

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Abstract

Bi-factor and second-order models based on copulas are proposed for item response data, where the items are sampled from identified subdomains of some larger domain such that there is a homogeneous dependence within each domain. Our general models include the Gaussian bi-factor and second-order models as special cases and can lead to more probability in the joint upper or lower tail compared with the Gaussian bi-factor and second-order models. Details on maximum likelihood estimation of parameters for the bi-factor and second-order copula models are given, as well as model selection and goodness-of-fit techniques. Our general methodology is demonstrated with an extensive simulation study and illustrated for the Toronto Alexithymia Scale. Our studies suggest that there can be a substantial improvement over the Gaussian bi-factor and second-order models both conceptually, as the items can have interpretations of discretized maxima/minima or mixtures of discretized means in comparison with discretized means, and in fit to data.

Item Type: Article
Additional Information: Acknowledgements: The simulations presented in this paper were carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.
Uncontrolled Keywords: bi-factor model,conditional independence,limited information,second-order model,asymmetry,truncated vines,psychology(all),applied mathematics ,/dk/atira/pure/subjectarea/asjc/3200
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Statistics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 01 Nov 2022 15:34
Last Modified: 05 Dec 2023 02:18
URI: https://ueaeprints.uea.ac.uk/id/eprint/89485
DOI: 10.1007/s11336-022-09894-2

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