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

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.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.