Factor tree copula models for item response data

Kadhem, Sayed H. and Nikoloulopoulos, Aristidis K. ORCID: https://orcid.org/0000-0003-0853-0084 (2023) Factor tree copula models for item response data. Psychometrika, 88 (3). pp. 776-802. ISSN 0033-3123

Preview

PDF (Kadhem-Nikoloulopoulos-2023b-Pka) - Published Version Available under License Creative Commons Attribution. Download (619kB) | Preview

Abstract

Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combined model, the so-called factor tree copula model, with individual benefits from each of the two approaches. Rather than adding factors and causing computational problems and difficulties in interpretation and identification, a truncated vine structure is assumed on the residuals conditional on one or two latent variables. This structure can be better explained as a conditional dependence given a few interpretable latent variables. On the one hand, the parsimonious feature of factor models remains intact and any residual dependencies are being taken into account on the other. We discuss estimation along with model selection. In particular, we propose model selection algorithms to choose a plausible factor tree copula model to capture the (residual) dependencies among the item responses. Our general methodology is demonstrated with an extensive simulation study and illustrated by analyzing Post-Traumatic Stress Disorder.

Item Type:	Article
Uncontrolled Keywords:	conditional dependence,factor copula models,latent variable models,truncated vine copula models,psychology(all),applied mathematics,4* ,/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:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	24 May 2023 11:32
Last Modified:	07 Nov 2024 12:46
URI:	https://ueaeprints.uea.ac.uk/id/eprint/92159
DOI:	10.1007/s11336-023-09917-6

Downloads

Actions (login required)

View Item