Browsing by Author "Tison, Emilee B."

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    Differential Prediction: Understanding a Tool for Detecting Rating Bias in Performance Ratings
    Tison, Emilee B. (Virginia Tech, 2008-03-19)
    Three common methods have been used to assess the existence of rating bias in performance ratings: the total association approach, the differential constructs approach and the direct effects approach. One purpose of this study was to examine how the direct effects approach, and more specifically differential prediction analysis, is more useful than the other two approaches in examining the existence of rating bias. However, the usefulness of differential prediction depends on modeling the full rater race X ratee race interaction. Therefore, the second purpose of this study was to examine the conditions where differential prediction has sufficient power to detect this interaction. This was accomplished using monte carlo simulations. Total sample size, magnitude of rating bias, validity of predictor scores, rater race proportion and ratee race proportion were manipulated to identify which conditions of these parameters provided acceptable power to detect the rater race X ratee race interaction; in the conditions where power levels are acceptable, differential prediction is a useful tool in examining the existence of rating bias. The simulation results suggest that total sample size, magnitude of rating bias and rater race proportion have the most impact on power levels. Furthermore, these three parameters interact to effect power. Implications of these results are discussed.
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    Towards a More Complete Understanding of Adverse Impact: Examining Issues of Minority Availability
    Tison, Emilee B. (Virginia Tech, 2010-09-03)
    Selection research often examines whether adverse impact can be reduced/eliminated from employment practices. Such research, however, largely ignores the influence of minority availability issues (i.e., the number of minorities who apply and the number of minorities who accept a job offer); three general factors comprise minority availability: the missing applicant problem, targeted recruitment and job refusal rates. As minority availability issues have not been systematically addressed in the broader literature, the purpose of this study was twofold: 1) to highlight the importance of and explicate a comprehensive description of their potential effects on adverse impact and 2) to demonstrate such effects through monte carlo simulations. Specifically, simulations were used to examine issues related to the level effects and covariance effects of minority availability on adverse impact. Therefore, an iterative process was used whereby minority availability factors were manipulated to produce combinations that meaningfully affect adverse impact; the goal was to conduct as many simulations as necessary to establish a reliable pattern of the effects of minority availability on adverse impact. Simulation results suggest minority availability issues can influence the detection of adverse impact. In fact, minority availability issues may hinder efforts to reduce adverse impact in some selection contexts. Implications of these results are discussed.