The results of meta-analytic (MA) and validity generalization (VG) studies continue to be impressive. In contrast to earlier findings that capped the variance accounted for in job performance at roughly 16%, many recent studies suggest that a single predictor variable can account for between 16 and 36% of the variance in some aspect of job performance. This article argues that this “enhancement” in variance accounted for is often attributable not to improvements in science but to a dumbing down of the standards for the values of statistics used in correction equations. With rare exceptions, applied researchers have suspended judgment about what is and is not an acceptable threshold for criterion reliability in their quest for higher validities. We demonstrate a statistical dysfunction that is a direct result of using low criterion reliabilities in corrections for attenuation. Corrections typically applied to a single predictor in a VG study are instead applied to multiple predictors. A multiple correlation analysis is then conducted on corrected validity coefficients. It is shown that the corrections often used in single predictor studies yield a squared multiple correlation that appears suspect. Basically, the multiple predictor study exposes the tenuous statistical foundation of using abjectly low criterion reliabilities in single predictor VG studies. Recommendations for restoring scientific integrity to the meta-analyses that permeate industrial–organizational (I–O) psychology are offered.

This article highlights the virtue of integrating well-being metrics (e.g., psychological well-being, perceived meaning) into aspects of utility analysis for the purpose of enhancing human resource management strategies and worker performance. We present the reader with a review of conceptual and practical developments in this field and examples of utility analysis calculations, while we advocate for the necessity of including well-being metrics in utility analysis for the 21st century. The basic thrust of this effort is to encourage the greater employment by managers of quantitative models that allow decision makers to generate all the factors needed to estimate long-term financial gains and/or losses before any intervention strategy is implemented in the workplace. As indicated, the use of quantitative models to estimate the net financial gains of using particular intervention strategies, accompanied with the value estimation of certain types of employee states (e.g., psychological well-being) and worker behaviors (e.g., employee turnover), can ultimately save companies from making gross tactical errors and, more positively, can assist management in promoting the organization's long-term economic goals in conjunction with the enhanced well-being of employees.