9 Use and Importance of Personality Variables in Work Settings

Industrial and Organizational Psychology


  1. Leaetta M. Hough PhD1,
  2. Jeff W. Johnson PhD2

Published Online: 26 SEP 2012

DOI: 10.1002/9781118133880.hop212009

Handbook of Psychology, Second Edition

Handbook of Psychology, Second Edition

How to Cite

Hough, L. M. and Johnson, J. W. 2012. Use and Importance of Personality Variables in Work Settings. Handbook of Psychology, Second Edition. 12:II:9.

Author Information

  1. 1

    The Dunnette Group, Ltd., St. Paul, Minnesota, USA

  2. 2

    Personnel Decisions Research Institute, Minneapolis, Minnesota, USA

Publication History

  1. Published Online: 26 SEP 2012


This chapter describes what we have learned in the past decade in the area of personality research in organizations and integrates it with what we learned before that has stood the test of time. Separate sections focus on (a) the structure of personality, (b) models of the determinants of work performance that include personality variables, (c) the magnitude of criterion-related validity of personality variables for predicting work-related outcomes, (d) effects of including personality measures for hiring and promotion decisions on demographic and ethnic groups, (e) different methods of measuring personality variables, and (f) effects of intentional distortion on validity and strategies for ameliorating its effects. We close with a summary of the evidence and suggestions for a path forward. We call for (a) the development of a meta-analytic database linking specific personality variables to a standard criterion taxonomy to be used for applying synthetic validation models, (b) further research exploring the mechanisms through which personality operates to influence individual performance, (c) the continued application of computer adaptive personality tests, and (d) more research into collecting personality ratings from other observers.


  • personality;
  • job performance;
  • personnel selection;
  • intentional distortion;
  • process models;
  • meta-analysis;
  • synthetic validation