Generalized IRT models for extreme response style

Kuan Yu JIN, Wen Chung WANG

Research output: Contribution to journalArticlespeer-review

41 Citations (Scopus)

Abstract

Extreme response style (ERS) is a systematic tendency for a person to endorse extreme options (e.g., strongly disagree, strongly agree) on Likert-type or rating-scale items. In this study, we develop a new class of item response theory (IRT) models to account for ERS so that the target latent trait is free from the response style and the tendency of ERS is quantified. Parameters of these new models can be estimated with marginal maximum likelihood estimation methods or Bayesian methods. In this study, we use the freeware program WinBUGS, which implements Bayesian methods. In a series of simulations, we find that the parameters are recovered fairly well; ignoring ERS by fitting standard IRT models resulted in biased estimates, and fitting the new models to data without ERS did little harm. Two empirical examples are provided to illustrate the implications and applications of the new models. Copyright © 2013 The Author(s).
Original languageEnglish
Pages (from-to)116-138
JournalEducational and Psychological Measurement
Volume74
Issue number1
Early online dateAug 2013
DOIs
Publication statusPublished - 2014

Citation

Jin, K.-Y., & Wang, W.-C. (2014). Generalized IRT models for extreme response style. Educational and Psychological Measurement, 74(1), 116–138.

Keywords

  • Item response theory
  • Extreme response style
  • Random threshold
  • Bayesian methods

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