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Area of Science:

  • Psychological Measurement
  • Psychometrics
  • Personality Assessment

Background:

  • Self-report personality tests are susceptible to faking, particularly in high-stakes situations like job applications.
  • Existing models may not fully capture the item-specific nature of response biases.
  • Accurate personality assessment is crucial for fair and effective decision-making.

Purpose of the Study:

  • To extend the multidimensional nominal response model (MNRM) to explicitly model and account for faking behavior in personality assessments.
  • To evaluate the performance of the extended MNRM in simulation studies and real-world high-stakes testing scenarios.
  • To demonstrate the utility of the model in improving the accuracy and validity of personality trait scores.

Main Methods:

  • Developed an extension of the multidimensional nominal response model (MNRM) incorporating a faking dimension.
  • Conducted simulation studies to assess parameter recovery and model selection performance.
  • Applied the extended MNRM to data from 3,046 job applicants in a high-stakes setting and job incumbents in a low-stakes setting.
  • Used item-specific scoring weights derived from a pilot study to model faking effects.

Main Results:

  • The extended MNRM demonstrated good parameter recovery in simulations.
  • Modeling faking significantly improved model fit and divergent validity compared to models without faking.
  • The faking dimension showed meaningful relationships with covariates.
  • The model effectively captured faking and adjusted trait scores in both high-stakes and low-stakes conditions.

Conclusions:

  • The extended MNRM provides a robust method for detecting and correcting faking in personality assessments.
  • Accounting for faking enhances the psychometric properties and validity of personality measures.
  • This approach has significant implications for improving the integrity of high-stakes assessment contexts.