This study provides meta-analytic estimates of the antecedents and consequences of feedbackseeking behavior (FSB). Clear support was found for the guiding cost/benefit framework in the feedback-seeking domain. Organizational tenure, job tenure, and age were negatively related to FSB. Learning and performance goal orientation, external feedback propensity, frequent positive feedback, high self-esteem, a transformational leadership style, and a high-quality relationship were positively associated with FSB. Challenging some of the dominant views in the feedback-seeking domain, the relationship between uncertainty and FSB was negative and the relationship between FSB and performance was small. Finally, inquiry and monitoring are not interchangeable feedback-seeking tactics. So FSB is best represented as an aggregate model instead of a latent model. In the discussion, gaps in the current FSB knowledge are identified and a research agenda for the future is put forward. Future research may benefit from (a) a 2013 Acknowledgments: We would like to thank Paul E. Levy for his valuable comments on a previous version of this article. Authors marked with an asterisk contributed equally; authorship was determined alphabetically. Winny Shen is now at the Department of Psychology, University of South Florida.

Predicting outcomes is critical in many domains of organizational research and practice. Over the past few decades, there have been substantial advances in predictive modeling methods and concepts from the computer science, machine learning, and statistics literatures that may have potential value for organizational science and practice. Nevertheless, treatment of these modern methods in major management and industrial-organizational psychology journals remains minimal. The purpose of this article is to (a) raise awareness among organizational researchers and practitioners with regard to several modern prediction methods and concepts, (b) discuss in nonmathematical terms how they compare to traditional regression-based prediction methods, and (c) provide an empirical example of their application and performance relative to traditional methods. Beyond illustrating their potential for improving prediction, we will also illustrate how these methods can offer deeper insights into how predictor content functions beyond simple construct-based explanations.

In a recent article, O'Boyle and Aguinis (2012) argued that job performance is not distributed normally but instead is nonnormal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used. To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (a) reflect behavior, (b) include an aggregation of multiple behaviors, (c) include the full range of performers, (d) include the full range of performance, (e) be time bounded, (f) focus on comparable jobs, and (g) not be distorted by motivational forces. Next, we present data from a wide range of sources-including the workplace, laboratory, athletics, and computer simulations-that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit. We conclude that large departures from normality are in many cases an artifact of measurement.

This article examines the role of socioeconomic status (SES) in the relationships among college admissions-test scores, secondary school grades, and subsequent academic performance. Scores on the SAT (a test widely used in the admissions process in the United States), secondary school grades, college grades, and SES measures from 143,606 students at 110 colleges and universities were examined, and results of these analyses were compared with results obtained using a 41-school data set including scores from the prior version of the SAT and using University of California data from prior research on the role of SES. In all the data sets, the SAT showed incremental validity over secondary school grades in predicting subsequent academic performance, and this incremental relationship was not substantially affected by controlling for SES. The SES of enrolled students was very similar to that of specific schools' applicant pools, which suggests that the barrier to college for low-SES students in the United States is a lower rate of entering the college admissions process, rather than exclusion on the part of colleges.