Faking Detection Improved: Adopting a Likert Item Response Process Tree Model

Tianjun Sun, Bo Zhang, Mengyang Cao, Fritz Drasgow

Research output: Contribution to journalArticlepeer-review

Abstract

With the increasing popularity of noncognitive inventories in personnel selection, organizations typically wish to be able to tell when a job applicant purposefully manufactures a favorable impression. Past faking research has primarily focused on how to reduce faking via instrument design, warnings, and statistical corrections for faking. This article took a new approach by examining the effects of faking (experimentally manipulated and contextually driven) on response processes. We modified a recently introduced item response theory tree modeling procedure, the three-process model, to identify faking in two studies. Study 1 examined self-reported vocational interest assessment responses using an induced faking experimental design. Study 2 examined self-reported personality assessment responses when some people were in a high-stakes situation (i.e., selection). Across the two studies, individuals instructed or expected to fake were found to engage in more extreme responding. By identifying the underlying differences between fakers and honest respondents, the new approach improves our understanding of faking. Percentage cutoffs based on extreme responding produced a faker classification precision of 85% on average.

Original languageEnglish (US)
Pages (from-to)490-512
Number of pages23
JournalOrganizational Research Methods
Volume25
Issue number3
DOIs
StatePublished - Jul 2022

Keywords

  • extreme response styles
  • faking
  • item response theory
  • selection assessment
  • tree process model

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

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