Individual differences at play: An investigation into measuring Big Five personality facets with game-based assessments

Felix Y. Wu, Evan Mulfinger, Leo Alexander, Andrea L. Sinclair, Rodney A. McCloy, Frederick L. Oswald

Research output: Contribution to journalArticlepeer-review

Abstract

Using game-based assessments (GBAs) to assess and select job applicants presents the dual challenges of measuring intended job-relevant constructs while analyzing GBA data that contain more predictors than observations. Exploring those challenges, we analyzed two GBAs that were designed to measure conscientiousness facets (i.e., achievement striving, self-discipline, and cautiousness). Scores on traditional measures of personality and cognitive ability were modeled using either a restricted set of GBA predictors using cross-validated ordinary least squares (OLS) regression or by the fuller set (p = 248) using random forests regression. Overall, the prediction of personality was near-zero; but the latter approach explained 14%–30% of the variance in predicting cognitive ability. Our findings warn of GBAs potentially measuring unintended constructs rather than their intended constructs.

Original languageEnglish (US)
Pages (from-to)62-81
Number of pages20
JournalInternational Journal of Selection and Assessment
Volume30
Issue number1
DOIs
StatePublished - Mar 2022
Externally publishedYes

Keywords

  • big data
  • cognitve ability
  • conscientiousness
  • game-based assessment
  • machine learning

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Applied Psychology
  • Psychology(all)
  • Strategy and Management
  • Management of Technology and Innovation

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