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

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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.
Original languageEnglish
Pages (from-to)132-157
Number of pages26
JournalPsychometrika
Volume88
Early online date21 Nov 2022
DOIs
Publication statusPublished - Mar 2023

Keywords

  • Bi-factor model
  • conditional independence
  • limited information
  • second-order model
  • tail dependence/asymmetry
  • truncated vines

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