Journal of Business and Psychology

, Volume 11, Issue 1, pp 101–112 | Cite as

The impact of four missing data techniques on validity estimates in human resource management

  • Philip L. Roth
  • James E. Campion
  • Steven D. Jones
Full Articles


While missing data are a commo problem in field settings, there is relatively little information in human Resource Management to guide researchers when they conduct analyses with incomplete data. This article discusses four techniques to deal wih missing data. The implications of using listwise deletion, pariwise deletion, mean substitution, and regression estimation are demonstrated in an applied selection situation. The importance of the manner in which data were missing is analyzed and discussed.


Resource Management Social Psychology Human Resource Social Issue Missing Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Afifi, A. A. & Elashoff, R. M. (1969). Missing observations in multivariate statistics III: Large sample analysis of simple linear regression.American Statistical Association Journal, March, 337–358.Google Scholar
  2. Beale, E. M. L., & Little, R. J. A. (1975). Missing values in multivariate analysis.Journal of the Royal Statistical Society, Series B, 37, 129–135.Google Scholar
  3. Buck, S. F. (1960). A method of estimation of missing values in multivariate data suitable for use with an electronic computer.Journal of the Royal Statistical Society B22, 302–306.Google Scholar
  4. Chan, L. S., & Dunn, O. J. (1972). The treatment of missing values in discriminant analysis-I. The sampling experiment.Journal of the American Statistical Association, 67(338), 473–477.Google Scholar
  5. Department of Labor Employment and Training Administration. (1977).Dictionary of Occupational Titles. Washington, D.C.: U.S. Government Printing Office.Google Scholar
  6. Gleason, T. C., & Staelin, R. (1975). A proposal for handling missing data.Psychometrika, 40(2), 229–252.Google Scholar
  7. Guertin, W. H. (1968). Comparisons of three methods of handling missing observations.Psychological Reports, 22, 896.Google Scholar
  8. Hunter, J. E. (1994). Commentary of Roth, Switzer, Campion, & Jones, In F. L. Schmidt (Chair),Advances inconstruct and criterion related validity research. Symposium presented at The Ninth Annual Conference for Industrial and Organizational Psychology, Nashville, TN.Google Scholar
  9. Kaufman, C. J. (1988). The application of logical imputation to household measurement.Journal of the Market Research Society, 30(4), 453–466.Google Scholar
  10. Kim, J., & Curry, J. (1977). The treatment of missing data in multivariate analysis.Sociological Methods & Research, 6(2), 215–240.Google Scholar
  11. Lepkowski, J.M., Landis, J. R., & Stehouwer, S. A. (1987). Strategies for the analysis of imputed data from a sample survey.Medical Care, 28(8), 705–716.Google Scholar
  12. Little, R. J. A. (1988). Missing data adjustments in large surveys.American Statistical Association, 6(3), 287–296.Google Scholar
  13. Little, R. J. A., & Rubin, D. B. (1987).Statistical analyses with missing data. New York: John Wiley & Sons.Google Scholar
  14. Raymond, M. R. (1986). Missing data in evaluation research.Evaluation & the Health Professions, 9(4), 395–420.Google Scholar
  15. Raymond, M. R., & Roberts, D. M. (1987). A comparison of methods for treating incomplete data in selection research.Educational and Psychological Measuremen, 47, 13–26.Google Scholar
  16. Roth, P. L. (1994). Missing data: Aconceptual review for applied psychologists.Personnel Psychology. 47, 537–560.Google Scholar
  17. Roth, P. L., & Campion, J. E. (1992). An analysis of the predictive power of the panel interview and preemployment tests.Journal of Occupational and Organizational Psychology, 65, 51–60.Google Scholar
  18. Roth, P. L., & Switzer, F. S. III. (1994). A Monte Carlo Analysis of Five Missing Data Techniques in an HRM Setting Manuscript submitted for publication.Google Scholar
  19. Schmidt, F. L., Hunter, J. E., & Urry, V. W. (1976). Statistical power in criterion related validation studies.Journal of Applied Psychology, 61, 473–485.Google Scholar

Copyright information

© Human Sciences Press, Inc 1996

Authors and Affiliations

  • Philip L. Roth
    • 3
  • James E. Campion
    • 1
  • Steven D. Jones
    • 2
  1. 1.University of HoustonUSA
  2. 2.Middle Tennessee State UniversityUSA
  3. 3.Department of Management, Sirrine HallClemson UniversityClemson

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