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Using natural language processing to increase prediction and reduce subgroup differences in personnel selection

Emily D Campion1, Michael A Campion2, James Johnson3

  • 1Department of Management and Entrepreneurship, University of Iowa.

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|October 19, 2023
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Summary
This summary is machine-generated.

Natural language processing (NLP) of applicant narratives improves job selection predictions and reduces racial disparities. This method uncovers job-related insights missed by traditional tests and numeric data.

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Area of Science:

  • Organizational Psychology
  • Natural Language Processing
  • Human Resources

Background:

  • Traditional selection methods rely on mental ability tests and numeric data.
  • These methods may not capture all relevant job-related constructs.
  • Existing methods can exhibit racial subgroup differences.

Purpose of the Study:

  • To demonstrate NLP's ability to enhance prediction using narrative application data.
  • To reduce racial subgroup differences in selection scores.
  • To identify uncaptured job-related constructs via NLP.

Main Methods:

  • Applied NLP to narrative application data from U.S. Air Force Officer Training School applicants (N=1,828).
  • Predicted selection decisions made by senior officer boards.
  • Compared NLP-derived scores against mental ability tests and numeric data.

Main Results:

  • NLP scores predicted board scores comparably to human raters (.60 correlation).
  • NLP added incremental predictive validity beyond traditional metrics.
  • NLP reduced racial subgroup differences in selection scores.

Conclusions:

  • NLP analysis of narrative data shows promise for improving selection validity.
  • This approach helps address the validity-adverse impact dilemma.
  • NLP offers a method to uncover deeper applicant insights.