Additive transformations are often offered as a remedy for the common problem of collinearity in moderated regression and polynomial regression analysis. As the authors demonstrate in this article, mean-centering reduces nonessential collinearity but not essential collinearity. Therefore, in most cases, mean-centering of predictors does not accomplish its intended goal. In this article, the authors discuss and explain, through derivation of equations and empirical examples, that mean-centering changes lower order regression coefficients but not the highest order coefficients, does not change the fit of regression models, does not impact the power to detect moderating effects, and does not alter the reliability of product terms. The authors outline the positive effects of mean-centering, namely, the increased interpretability of the results and its importance for moderator analysis in structural equations and multilevel analysis. It is recommended that researchers center their predictor variables when their variables do not have meaningful zero-points within the range of the variables to assist in interpreting the results.

The personality trait of conscientiousness has seen considerable attention from applied psychologists due to its efficacy for predicting job performance across performance dimensions and occupations. However, recent theoretical and empirical developments have questioned the assumption that more conscientiousness always results in better job performance, suggesting a curvilinear link between the 2. Despite these developments, the results of studies directly testing the idea have been mixed. Here, we propose this link has been obscured by another pervasive assumption known as the dominance model of measurement: that higher scores on traditional personality measures always indicate higher levels of conscientiousness. Recent research suggests dominance models show inferior fit to personality test scores as compared to ideal point models that allow for curvilinear relationships between traits and scores. Using data from 2 different samples of job incumbents, we show the rank-order changes that result from using an ideal point model expose a curvilinear link between conscientiousness and job performance 100% of the time, whereas results using dominance models show mixed results, similar to the current state of the literature. Finally, with an independent cross-validation sample, we show that selection based on predicted performance using ideal point scores results in more favorable objective hiring outcomes. Implications for practice and future research are discussed.

More attention is being given to the stable, dispositional tendency to maximize when making decisions. Within the growing body of research has been an exchange regarding the appropriate means of measuring maximizing tendency. Guided primarily by psychometric and statistical analyses, these studies have critiqued and revised existing maximizing tendency scales and/or introduced new measures. Importantly, many of these discussions seem to ignore theoretical considerations of the construct of maximizing. In this article, we revisit the original work of Herbert Simon, from where the theory of maximizing tendency was developed, and provide a theoretical account for how maximizers can be distinguished from satisficers and the implications therein for the measurement of maximizing tendency. Across two studies, we provide an updated psychometric, correlational, and behavioral comparison of the two most popular maximizing tendency scales: the Maximization Scale and the Maximizing Tendency Scale. Results demonstrate that the Maximizing Tendency Scale is more theoretically and psychometrically valid than the Maximization Scale. Copyright © 2015 John Wiley & Sons, Ltd.Additional supporting information may be found in the online version of this article at the publisher's web-site.key words maximizing tendency; maximize; satisfice; theoretical evaluation; psychometric evaluationOver the past decade, increased attention has been given to the dispositional tendency to maximize; within this growing body of research is a debate regarding the proper measurement of maximizing tendency. What has been given less attention, however, is the theory of maximizing and how it relates to the measurement of maximizing tendency. Indeed, many of the articles have attempted to identify the best measure of maximizing tendency based primarily on psychometric evidence ignoring the theory of the construct. This is problematic insofar as appropriate measurement of psychological variables hinges upon sound theories of constructs. The primary purpose of this article, therefore, is to present a theoretical justification for the measurement of maximizing tendency. The secondary purpose is to provide a comparison of the two most popular maximizing tendency scales: the Maximization Scale (MS; Schwartz et al., 2002) and the Maximizing Tendency Scale (MTS; Diab, Gillespie, & Highhouse, 2008). Across two studies, we examine which scale performs better psychometrically and theoretically. The theory of maximizing tendencyThe concept of maximizing was derived from Simon's (1955Simon's ( , 1956) work on satisficing. Simon argued that the normative, rational model of decision making-wherein decision-makers consider all alternatives, choosing the utility-maximizing option-was incompatible with the limits of basic human information and computational processing.Stated differently, humans could not optimize their decisions as would be suggested by the rational-man model of decision making. Instead, Simon argued, individuals elected to find an alternative that ...

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Communicating the results of research to nonscientists presents many challenges. Among these challenges is communicating the effectiveness of an intervention in a way that people untrained in statistics can understand. Use of traditional effect size metrics (e.g., r, r²) has been criticized as being confusing to general audiences. In response, researchers have developed nontraditional effect size indicators (e.g., binomial effect size display, common language effect size indicator) with the goal of presenting information in a more understandable manner. The studies described here present the first empirical test of these claims of understandability. Results show that nontraditional effect size indicators are perceived as more understandable and useful than traditional indicators for communicating the effectiveness of an intervention. People also rated training programs as more effective and were willing to pay more for programs whose effectiveness was described using the nontraditional effect size metrics.