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Systematic Data Loss in HRM Settings: A Monte Carlo Analysis

Fred S. Switzer, III

Philip L. Roth

Deborah M. Switzer

Clemson University

The accuracy of eight missing data techniques (MDTs) under conditions of systematically missing data was tested using a Monte Carlo analysis. Data were generated from a population correlation matrix, then deleted using several patterns that might be found in a human resource management (HRM) selection validation study. The results indicated that listwise and pairwise deletion were the most accurate methods, followed closely by imputation methods such as regression and hot-deck. Mean substitution was substantially inferior to the other methods tested. Future research that examines different missing data patterns is recommended.

Journal of Management, Vol. 24, No. 6, 763-779 (1998)
DOI: 10.1177/014920639802400605
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