Mixture item response models for inattentive responding behavior

Kuan Yu JIN, Hui Fang CHEN, Wen Chung WANG

Research output: Contribution to journalArticlespeer-review

15 Citations (Scopus)


Inattentive responses can threaten measurement quality, yet they are common in rating- or Likert-scale data. In this study, we proposed a new mixture item response theory model to distinguish inattentive responses from normal responses so that test validity can be ascertained. Simulation studies demonstrated that the parameters of the new model were recovered fairly well using the Bayesian methods implemented in the freeware WinBUGS, and fitting the new model to data that lacked inattentive responses did not result in severely biased parameter estimates. In contrast, ignoring inattentive responses by fitting standard item response theory models to data containing inattentive responses yielded seriously biased parameter estimates and a failure to distinguish inattentive participants from normal participants; the person-fit statistic lz was also unsatisfactory in identifying inattentive responses. Two empirical examples demonstrate the applications of the new model. Copyright © 2017 The Author(s). 
Original languageEnglish
Pages (from-to)197-225
JournalOrganizational Research Methods
Issue number1
Early online dateAug 2017
Publication statusPublished - 2018


Jin, K.-Y., Chen, H.-F., & Wang, W.-C. (2018). Mixture item response models for inattentive responding behavior. Organizational Research Methods, 21(1), 197-225.


  • Item response theory
  • Quantitative research
  • Survey research
  • Measurement models


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