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The Equal Employment Opportunity Commission's 4/5ths rule has been used for over 20 years in applied psychology and employment law. The rule signals that there is adverse impact when the protected group selection ratio is less than 80% of the highest scoring group's selection ratio. We conducted several simulations and found, consistent with some previous management science literature, that the 4/5ths rule often resulted in false-positive readings of adverse impact even when there were no underlying (population) standardized group differences between subgroups. We then incorporated tests of statistical significance and found that adding such tests to the 4/5ths rule eliminated many false-positive indications of adverse impact. We also examined simulated selection systems based on meta-analytic values from the selection literature. The frequency of adverse impact signals from the 4/5ths rule increased markedly relative to simulations with no subgroup population differences. Adding statistical tests mitigated the number of indications of adverse impact to some extent. Keywords: 4/5ths rule, adverse impact, test bias, employment discrimination

This meta-analysis evaluated predictors of both objective and subjective sales performance. Biodata measures and sales ability inventories were good predictors of the ratings criterion, with corrected rs of .52 and .45, respectively. Potency (a subdimension of the Big 5 personality dimension Extraversion) predicted supervisor ratings of performance (r = .28) and objective measures of sales (r = .26). Achievement (a component of the Conscientiousness dimension) predicted ratings (r = .25) and objective sales (r = .41). General cognitive ability showed a correlation of .40 with ratings but only .04 with objective sales. Similarly, age predicted ratings (r = .26) but not objective sales (r = -.06). On the basis of a small number of studies, interest appears to be a promising predictor of sales success.

The effects of placement of crew's communication and coordination training for teams on system performance and communication were examined using 40 two-person teams operating an industrial process-control simulator. Analysis indicated that the best performance resulted when operators were given communication and coordination training during or after task training had occurred. However, performance differences were not directly due to the number or type of communications exchanged between operators. The difference in performance is probably attributable to differences in the memory load of the conditions or in the mental models adopted by the subjects.

Bootstrapping is introduced as a method for approximating the standard errors of validity generalization (VG) estimates. A Monte Carlo study was conducted to evaluate the accuracy of bootstrap validity-distribution parameter estimates, and nonparametric bootstrap confidence intervals. In the simulation study we manipulated the sample sizes per correlation coefficient, the number of coefficients per VG analysis, and the variance of the distribution of true correlation coefficients. The results indicate that the standard error estimates produced by the bootstrapping procedure were very accurate. It is recommended that the bootstrap standard-error estimates and confidence intervals be used in the interpretation of the results of VG analyses. (Reprinted by permission of the publisher.)

A Monte Carlo study was conducted to determine how variations in true reliability distributions affect validity generation estimates that are based on hypothetical reliability distributions proposed by Schmidt and Hunter. Both interactive and noninteractive validity generalization equations were examined. True reliability distributions and sample sizes were systematically varied. Depending on the sample size and nature of the true distributions, artifactual variance was either overestimated or underestimated by both equations. In some cases, extremely large errors were observed. Even when the data were generated with true reliability distributions identical to the hypothetical distributions, both equations systematically overestimated artifactual variance. The severity of estimation errors increased proportionally as sample size per validity coefficient increased. Implications of using assumed distributions and recommendations for validity generalization researchers are discussed. (Reprinted by permission of the publisher.)