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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 AI Faculty of Science > Research Groups > Statistics (former - to 2024) Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 01 Nov 2022 15:34 |
| Last Modified: | 07 Nov 2024 12:45 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/89485 |
| DOI: | 10.1007/s11336-022-09894-2 |
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