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 cognitive ability levels of different ethnic groups have interested psychologists for over a century. Many narrative reviews of the empirical literature in the area focus on the Black-White differences, and the reviews conclude that the mean difference in cognitive ability (9) is approximately 1 standard deviation; that is, the generally accepted effect size is about 1.0. We conduct a meta-analytic review that suggests that the one standard deviation effect size accurately summarizes Black-White differences for college application tests (e.g., SAT) and overall analyses of tests of g for job applicants in corporate settings. However, the 1 standard deviation summary of group differences fails to capture many of the complexities in estimating ethnic group differences in employment settings. For example, our results indicate that job complexity, the use of within job versus across job study design, focus on applicant versus incumbent samples, and the exact construct of interest are important moderators of standardized group differences. In many instances, standardized group differences are less than 1 standard deviation. We conduct similar analyses for Hispanics, when possible, and note that Hispanic-White differences are somewhat less than Black-White differences.Ethnic group differences on measures of cognitive ability have been investigated by some of the earliest social science researchers (e.g., Galton, 1892;Thorndike, 1921) and this topic continues to receive a great

Researchers in many fields use multiple item scales to measure important variables such as attitudes and personality traits, but find that some respondents failed to complete certain items. Past missing data research focuses on missing entire instruments, and is of limited help because there are few variables to help impute missing scores and the variables are often not highly related to each other. Multiple item scales offer the unique opportunity to impute missing values from other correlated items designed to measure the same construct. A Monte Carlo analysis was conducted to compare several missing data techniques. The techniques included listwise deletion, regression imputation, hot-deck imputation, and two forms of mean substitution. Results suggest that regression imputation and substituting the mean response of a person to other items on a scale are very promising approaches. Furthermore, the imputation techniques often outperformed listwise deletion.

Employers and academics have differing views on the value of grades for predicting job performance. Employers often believe grades are useful predictors, and they make hiring decisions that are based on them. Many academics believe that grades have little predictive validity. Past meta-analyses of the grades-performance relationship have suffered either from small sample sizes or the inability to correct observed correlations for research artifacts. This study demonstrated the observed correlation between grades and job performance was .16. Correction for research artifacts increased the correlation to the .30s. Several factors were found to moderate the relationship. The most powerful factors were the year of research publication and the time between graduation and performance measurement. There has been considerable disagreement as to whether grades predict job performance. In general, employers have believed that grades help them understand who will perform a job well (Campion, 1978; Zikmund, Hitt, & Pickens, 1978). Employers have argued that grades are useful predictors because they reflect intelligence, motivation, and other abilities applicable to the job (Baird, 1985). Many employers screen applicants with a minimum grade point average (GPA) or heavily weighted grades when analyzing resumes (Dipboye, Fromkin, & Wiback, 1975;Reilly & Warech, 1993). Many academics have contended that grades are not good predictors of job performance (e.g., Calhoon & Reddy, 1968; Nelson, 1975). Nelson (1975) argued that there were situations in which skills learned in college were not required by the job or skills not learned in college courses affected job performance (e.g., social skills). They also argued that grades varied as a function of the

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.