Multivariate analysis of variance (MANOVA) is often categorized as a tool for experimental psychologists. However, it also continues to be a popular statistical procedure used by organizational scientists. Unfortunately, when the dependent variables (DV) are correlated with one another, interpreting the significant omnibus test in MANOVA becomes difficult. The present article proposes a novel way of interpreting a significant MANOVA that draws from work dedicated to understanding the relative importance of correlated predictors in multiple regression. Relative importance analyses are specifically designed to overcome the limitations caused by correlated variables and permit researchers to appropriately partition shared variance. We derive and extend relative weight analysis to MANOVA designs and demonstrate how these weights may be used to draw inferences concerning the relative contribution of each DV to the overall multivariate effect. Through our example, we illustrate how researchers must consider the correlations among the DVs when interpreting a significant multivariate effect, and our procedure provides an effective mechanism for doing just that.

For years, organizational scholars have sought effective ways to evaluate the importance of predictors included in a regression analysis. Recent techniques, such as general dominance weights and relative weights, have shown great promise for guiding evaluations of predictor importance. Nevertheless, questions remain regarding how one should investigate relative importance in the presence of a multidimensional criterion variable. The purpose of this article is to extend understanding of relative importance statistics to multivariate designs. The authors review the concept of relative importance and discuss a new procedure for calculating estimates of importance in the presence of multiple correlated criteria. Finally, a published correlation matrix is reanalyzed and a Monte Carlo simulation conducted to compare the new procedure with another technique for estimating importance. Unlike canonical solutions, which are often uninterpretable, the proposed multivariate relative weights provide an intuitive index regarding the relationship between predictors and criteria. Implications for organizational researchers are discussed.

This study examined how specific features of adaptive tests are related to test takers' reactions. Participants took a computer-adaptive test in which 2 features, difficulty of the initial item and difficulty of subsequent items, were manipulated, then responded to questionnaires assessing their reactions to the test. The data show that the relationship between a test's objective difficulty, which was determined by the 2 manipulated test characteristics, and reactions was fully mediated by perceived performance. Additional analyses evaluated the impact of feedback on reactions to the adaptive test. In general, feedback that was consistent with perceptions of performance was positively related to reactions. The results suggest that minor changes to the design of an adaptive test may potentially enhance examinees' reactions.

Prior research suggests that segregation in the U.S. workplace is on the rise (Hellerstein, Neumark, & McInerney, 2008); as such, leaders are more likely to lead groups of followers composed primarily of their own race (Elliot & Smith, 2001; Smith & Elliott, 2002). Drawing from theory on stigma-by-association, the authors posit that such segregated proximal social contexts (i.e., the leader's group of followers) can have detrimental effects on leader appraisals. Specifically, they argue that leaders of mostly Black follower groups experience stigmatization based on race stereotypic beliefs, which affects how they are viewed in the eyes of observers. The results of a large field study show performance evaluations generally tend to be lower when the proportion of Black followers is higher. Moreover, 3 experiments demonstrate that the impact of proximal social contexts extends to other outcomes (i.e., perceptions of market value and competency) but appears limited to those who are less internally and externally motivated to control their prejudice. Taken together, these findings explain how workplace segregation systematically can create a particular disadvantage for Black leaders.

The psychometric soundness of measures has been a central concern of articles published in the Journal of Applied Psychology (JAP) since the inception of the journal. At the same time, it isn't clear that investigators and reviewers prioritize psychometric soundness to a degree that would allow one to have sufficient confidence in conclusions regarding constructs. The purposes of the present article are to (a) examine current scale development and evaluation practices in JAP; (b) compare these practices to recommended practices, previous practices, and practices in other journals; and (c) use these comparisons to make recommendations for reviewers, editors, and investigators regarding the creation and evaluation of measures including Excel-based calculators for various indices. Finally, given that model complexity appears to have increased the need for short scales, we offer a user-friendly R Shiny app (https://orgscience.uncc.edu/about-us/resources) that identifies the subset of items that maximize a variety of psychometric criteria rather than merely maximizing alpha. (PsycInfo Database Record