Higher-Order Item Response Models for Hierarchical Latent Traits

Hung Yu Huang, Wen Chung Wang*, Po Hsi Chen, Chi Ming Su

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.

Original languageEnglish
Pages (from-to)619-637
Number of pages19
JournalApplied Psychological Measurement
Issue number8
Publication statusPublished - 2013 Nov


  • Bayesian
  • MCMC
  • hierarchical models
  • item response theory
  • multidimensional item response theory

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)


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