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In the structural model, the joint distribution of latent traits is defined via a copula with tail dependence. An expectation\u2013maximization algorithm with stochastic approximation is implemented for parameter estimation. A simulation study evaluates the proposed method\u2019s accuracy in ability estimation under various conditions, comparing it to traditional approaches. By better capturing the dependence structure between latent ability and speed, the proposed method outperforms the traditional approach with multivariate normality assumption. Applied to a variety of empirical datasets, the proposed method\u2019s flexibility and the improvement in model fit are illustrated. The copula-based latent variable modeling can be generalized to broader applications in psychological testing involving multivariate latent traits.<\/jats:p>","DOI":"10.1017\/psy.2026.10143","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:25:29Z","timestamp":1792131929000},"page":"1-57","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["A Copula-Based Joint Model for Speed and Accuracy"],"prefix":"10.1017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4073-6218","authenticated-orcid":false,"given":"Sunbeom","family":"Kwon","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/047426m28","id-type":"ROR","asserted-by":"publisher"}],"name":"Psychology, University of Illinois 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