Higher-order item response models for hierarchical latent traits

Hung-Yu HUANG, Wen Chung WANG, Po-Hsi CHEN, Chi-Ming SU

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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. Copyright © 2013 The Author(s) .
Original languageEnglish
Pages (from-to)619-637
JournalApplied Psychological Measurement
Issue number8
Early online dateMay 2013
Publication statusPublished - Nov 2013


Huang, H.-Y., Wang, W.-C., Chen, P.-H., & Su, C.-M. (2013). Higher-order item response models for hierarchical latent traits. Applied Psychological Measurement, 37(8), 619-637.


  • Item response theory
  • Bayesian
  • Hierarchical models
  • MCMC
  • Multidimensional item response theory


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