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Same Test, Better Scores: Boosting the Reliability of Short Online Intelligence Recruitment Tests with Nested Logit Item Response Theory Models

Author

Listed:
  • Martin Storme

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Nils Myszkowski

    (Department of Psychology, Pace University)

  • Simon Baron

    (Assess First)

  • David Bernard

    (Assess First)

Abstract

Assessing job applicants' general mental ability online poses psychometric challenges due to the necessity of having brief but accurate tests. Recent research (Myszkowski & Storme, 2018) suggests that recovering distractor information through Nested Logit Models (NLM; Suh & Bolt, 2010) increases the reliability of ability estimates in reasoning matrix-type tests. In the present research, we extended this result to a different context (online intelligence testing for recruitment) and in a larger sample ( N=2949 job applicants). We found that the NLMs outperformed the Nominal Response Model (Bock, 1970) and provided significant reliability gains compared with their binary logistic counterparts. In line with previous research, the gain in reliability was especially obtained at low ability levels. Implications and practical recommendations are discussed.

Suggested Citation

  • Martin Storme & Nils Myszkowski & Simon Baron & David Bernard, 2019. "Same Test, Better Scores: Boosting the Reliability of Short Online Intelligence Recruitment Tests with Nested Logit Item Response Theory Models," Post-Print hal-03001692, HAL.
    • Handle: RePEc:hal:journl:hal-03001692
    DOI: 10.3390/jintelligence7030017
    as

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