PROGRESS IN PSYCHOMETRICS: CAN INDUSTRIAL AND ORGANIZATIONAL PSYCHOLOGY CATCH UP?

Murphy and DeShon (2000) show that interrater correlations do not provide reasonable estimates of the reliability of job performance ratings, and suggest that better estimates can be obtained by applying the methods of generalizability theory. Schmidt, Viswesvaran, and Ones (2000) criticize our suggestions as radical, and argue that: (a) the reliability of ratings should be evaluated using the parallel test model rather than the more general and more realistic generalizability model, (b) reliability and validity are distinct concepts that should not be confused, and (c) measurement models have little to do with substantive models of the processes that generate scores on a test or measure. All three of these ideas were once part of the psychometric mainstream, but progress in psychometrics over the last 3 decades has moved the field well beyond these assumptions and approaches. Modern psychometric theory calls for close linkages between measurement models and substantive models of the phenomena being measured.     

Can Changes in Differential Dropout Rates Reduce Adverse Impact? A Computer Simulation Study of a Multi‐wave Selection System

A simulation was used to explore the effects of variations in the rate at which applicants drop out of selection processes on racial differences in selection outcomes. Archival data was used to simulate a realistic range of selection scenarios in which test score differences between groups and selection ratios varied. The basis for dropping out was manipulated in two separate studies. Study 1 simulated dropout decisions that occurred at random within racial subgroups; in this study, dropout rates of minority versus White candidates were varied. Study 2 examined dropout decisions that occurred as a function of test scores. Results from both studies showed that mean test score differences between White and minority applicants have the largest influence on adverse impact. Interventions designed to reduce the tendency of minority applicants to withdraw from selection are likely to have, at best, small effects on the adverse impact of selection tests.     

ARE WE GETTING FOOLED AGAIN? COMING TO TERMS WITH LIMITATIONS IN THE USE OF PERSONALITY TESTS FOR PERSONNEL SELECTION

We recently published an article in which we highlighted a number of issues associated with the use of self-report personality tests in personnel selection contexts ( Morgeson et al., 2007 ). Both Ones, Dilchert, Viswesvaran, and Judge (2007) and Tett and Christiansen (2007) have written responses to this article. In our response to these articles we address many of the issues raised by Ones et al. and Tett and Christiansen. In addition to a detailed response, we make the following 4 key points: (1) Our criticisms of personality testing apply only to the selection context, not to all research on personality; (2) the observed validities of personality tests predicting job performance criteria are low and have not changed much over time; (3) when evaluating the usefulness of using personality tests to select applicants, one must not ignore the observed, uncorrected validity; and (4) when discussing the value of personality tests for selection contexts, the most important criteria are those that reflect job performance. Implications for personality testing research and practice are discussed.     

Feminism and Religion

Religion—capital R—effectively eliminated the feminine experience in its process of institutionalization as church; religion—small r—the whole human view, cannot be adequately reflected through such a myopic institution. All humans will not be equally respected, given unequal status. Thus, the church becomes a powerful contributor to inherently unjust social, legal, and economic systems. Feminism, negated as a philosophical and religious world view by Western religions, challenges the status quo insofar as it calls for endorsement of full human equality.     

COLLEGE STUDENTS'ATTITUDES TOWARD EMPLOYEE DRUG TESTING PROGRAMS

Drug testing programs are becoming increasingly popular, but little is known about attitudes toward various aspects of common testing programs. This study examined college students'attitudes toward several aspects of drug testing programs. Results indicate that testing is seen, under some circumstances, as appropriate and necessary. However, attitudes toward most aspects of testing were characterized by extreme variability. Virtually all aspects of drug testing programs are strongly accepted by some individuals and strongly rejected by others. Furthermore, attitudes toward employee drug testing do not appear to vary as a function of employment experience, qualifications, or political inclinations, and are not strongly related to the individual's exposure to others'drug use. One of the few consistent correlates of approval of employee drug testing was the individual's drug use; self-report frequency of drug use was negatively correlated with the acceptability of employee drug testing.     

FOOLING YOURSELF WITH CROSS-VALIDATION: SINGLE SAMPLE DESIGNS

The most commonly used cross-validation design involves drawing a single sample and partitioning that sample into derivation and holdout subsamples. This type of design allows one to adjust for random sampling error, but like formula estimates of cross-validity, is insensitive to violations of sampling assumptions. As is shown in a small Monte Carlo study, results obtained in non-representative samples, which are known to be invalid in the population, will nonetheless hold up well under cross-validation when single-sample designs are employed. It is suggested that single-sample cross-validation estimates possess no clear-cut advantages over formula estimates, and thus are not worth the effort or the loss of degrees of freedom.