{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["88(1)"],"submitter":["Kadhem SH"],"pubmed_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."],"journal":["Psychometrika"],"pagination":["132-157"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9977904"],"repository":["biostudies-literature"],"pubmed_title":["Bi-factor and Second-Order Copula Models for Item Response Data."],"pmcid":["PMC9977904"],"pubmed_authors":["Kadhem SH","Nikoloulopoulos AK"],"additional_accession":[]},"is_claimable":false,"name":"Bi-factor and Second-Order Copula Models for Item Response Data.","description":"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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-04-22T21:47:51.659Z","creation":"2025-04-06T03:43:10.293Z"},"accession":"S-EPMC9977904","cross_references":{"pubmed":["36414825"],"doi":["10.1007/s11336-022-09894-2"]}}