Abstract
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.
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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.
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.
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.
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.
Department of Labor Employment and Training Administration. (1977).Dictionary of Occupational Titles. Washington, D.C.: U.S. Government Printing Office.
Gleason, T. C., & Staelin, R. (1975). A proposal for handling missing data.Psychometrika, 40(2), 229–252.
Guertin, W. H. (1968). Comparisons of three methods of handling missing observations.Psychological Reports, 22, 896.
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.
Kaufman, C. J. (1988). The application of logical imputation to household measurement.Journal of the Market Research Society, 30(4), 453–466.
Kim, J., & Curry, J. (1977). The treatment of missing data in multivariate analysis.Sociological Methods & Research, 6(2), 215–240.
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.
Little, R. J. A. (1988). Missing data adjustments in large surveys.American Statistical Association, 6(3), 287–296.
Little, R. J. A., & Rubin, D. B. (1987).Statistical analyses with missing data. New York: John Wiley & Sons.
Raymond, M. R. (1986). Missing data in evaluation research.Evaluation & the Health Professions, 9(4), 395–420.
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.
Roth, P. L. (1994). Missing data: Aconceptual review for applied psychologists.Personnel Psychology. 47, 537–560.
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.
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.
Schmidt, F. L., Hunter, J. E., & Urry, V. W. (1976). Statistical power in criterion related validation studies.Journal of Applied Psychology, 61, 473–485.
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The authors would like to thank Patricia G. Roth and Tim Summers (bot of Clemson University) as well as Joe Ward (University of Texas-Sa Antonio) for substantive comments on drafts of this article. The authors also appreciate the comments of Rich Arvey on the field of missing data. Diane Segal deserves thanks for her help conducting analyses. Their efforts have greatly enhanced the quality of this article.
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Roth, P.L., Campion, J.E. & Jones, S.D. The impact of four missing data techniques on validity estimates in human resource management. J Bus Psychol 11, 101–112 (1996). https://doi.org/10.1007/BF02278259
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DOI: https://doi.org/10.1007/BF02278259