Generalized IRT Models for Extreme Response Style

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.