Advancing and Evaluating IRT Model Data Fit Indices in Organizational Research

Christopher D. Nye, Seang Hwane Joo, Bo Zhang, Stephen Stark

Research output: Contribution to journalArticlepeer-review


Item response theory (IRT) models have a number of advantages for developing and evaluating scales in organizational research. However, these advantages can be obtained only when the IRT model used to estimate the parameters fits the data well. Therefore, examining IRT model fit is important before drawing conclusions from the data. To test model fit, a wide range of indices are available in the IRT literature and have demonstrated utility in past research. Nevertheless, the performance of many of these indices for detecting misfit has not been directly compared in simulations. The current study evaluates a number of these indices to determine their utility for detecting various types of misfit in both dominance and ideal point IRT models. Results indicate that some indices are more effective than others but that none of the indices accurately detected misfit due to multidimensionality in the data. The implications of these results for future organizational research are discussed.

Original languageEnglish (US)
Pages (from-to)457-486
Number of pages30
JournalOrganizational Research Methods
Issue number3
StatePublished - Jul 2020


  • item response theory
  • measurement models
  • quantitative research
  • survey research

ASJC Scopus subject areas

  • General Decision Sciences
  • Strategy and Management
  • Management of Technology and Innovation


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