Robert E. Ployhart

Applicants’ Perceptions of Selection Procedures and Decisions: A Critical Review and Agenda for the Future:

This review critically examines the literature from 1985 to 1999 on applicant perceptions of selection procedures. We organize our review around several key questions: What perceptions have been studied? What are determinants of perceptions? What are the consequences or outcomes associated with perceptions applicants hold? What theoretical frameworks are most useful in examining these perceptions? For each of these questions, we provide suggestions for key research directions. We conclude with a discussion of the practical implications of this line of research for those who design and administer selection processes.

Using personality testing to reduce adverse impact : A cautionary note

Adding a personality test to an ability test for employee selection has been suggested as a means of enhancing validity while reducing adverse impact of the selection system. Adverse impact was examined with different weightings of ability and personality testing at varying selection ratios using data from two large applicant samples. This demonstration indicated that the use of personality testing did not compensate for the adverse impact related to cognitive ability testing and that applicant pool characteristics and the nature of the personality measure may be important considerations. Overall, results suggest caution in presuming a reduction in adverse impact by the addition of personality measures.

The Estimation of Reliability in Longitudinal Models

Despite the increasing attention devoted to the study and analysis of longitudinal data, relatively little consideration has been directed toward understanding the issues of reliability and measurement error. Perhaps one reason for this neglect has been that traditional methods of estimation (e.g. generalisability theory) require assumptions that are often not tenable in longitudinal designs. This paper first examines applications of generalisability theory to the estimation of m easurement error and reliability in longitudinal research, and notes how factors such as missing data, correlated errors, and true score instability prohibit traditional variance com ponent estimation. Next, we discuss how estimation methods using restricted maximum likelihood can account for these factors, thereby providing m any advantages over traditional estimation methods. Finally, we provide a substantive exam ple illustrating these advantages, and include brief discussions of programming and software considerations.