Toward Better Meta-Analytic Matrices: How Input Values Can Affect Research Conclusions in Human Resource Management Simulations

Abstract

Simulations and analyses based on meta-analytic matrices are fairly common in human resource management and organizational behavior research, particularly in staffing research. Unfortunately, the meta-analytic values estimates for validity and group differences (i.e., ρ and δ, respectively) used in such matrices often vary in the extent to which they are affected by artifacts and how accurately the values capture the underlying constructs and the appropriate population. We investigate how such concerns might influence conclusions concerning key issues such as prediction of job performance and adverse impact of selection procedures, as well as noting wider applications of these issues. We also start the process of building a better matrix upon which to base many such simulations and analyses in staffing research. Finally, we offer guidelines to help researchers/practitioners better model human resources processes, and we suggest ways that researchers in a variety of areas can better assemble meta-analytic matrices.