Multilevel modeling allows researchers to understand whether relationships between lower-level variables (e.g., individual job satisfaction and individual performance, firm capabilities and performance) change as a function of higher-order moderator variables (e.g., leadership climate, market-based conditions). We describe how to estimate such cross-level interaction effects and distill the technical literature for a general readership of management researchers, including a description of the multilevel model building process and an illustration of analyses and results with a data set grounded in substantive theory. In addition, we provide 10 specific best-practice recommendations regarding persistent and important challenges that researchers face before and after data collection to improve the accuracy of substantive conclusions involving cross-level interaction effects. Our recommendations provide guidance on how to define the cross-level interaction effect, compute statistical power and make research design decisions, test hypotheses with various types of moderator variables (e.g., continuous, categorical), rescale (i.e., center) predictors, graph the cross-level interaction effect, interpret interactions given the symmetrical nature of such effects, test multiple cross-level interaction hypotheses, test cross-level interactions involving more than two levels of nesting, compute effect-size estimates and interpret the practical importance of a crosslevel interaction effect, and report results regarding the multilevel model building process.

The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier definitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, interesting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommendations can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.

SummaryAn interaction effect indicates that a relationship is contingent upon the values of another (moderator) variable. Thus, interaction effects describe conditions under which relationships change in strength and/or direction. Understanding interaction effects is essential for the advancement of the organizational sciences because they highlight a theory's boundary conditions. We describe procedures for estimating and interpreting interaction effects using moderated multiple regression (MMR). We distill the technical literature for a general readership of organizational science researchers and include specific best-practice recommendations regarding actions researchers can take before and after data collection to improve the accuracy of MMR-based conclusions regarding interaction effects.

SummaryOne of the key advantages of meta-analysis (i.e., a quantitative literature review) over a narrative literature review is that it allows for formal tests of interaction effects-namely, whether the relationship between two variables is contingent upon the value of another (moderator) variable. Interaction effects play a central role in organizational science research because they highlight boundary conditions of a theory: Conditions under which relationships change in strength and/or direction. This article describes procedures for estimating interaction effects using meta-analysis, distills the technical literature for a general readership of organizational science researchers, and includes specific best-practice recommendations regarding actions researchers can take before and after data collection to improve the accuracy of substantive conclusions regarding interaction effects investigated meta-analytically.

There are competing theoretical rationales and mechanisms used to explain the relation between leadership behaviors (e.g., consideration, initiating structure, contingent rewards, and transformational leadership) and follower performance (e.g., task performance and organizational citizenship behaviors). We conducted two studies to critically examine and clarify the leadership behaviors-follower performance relation by pitting the various theoretical rationales and mechanisms against each other. We first engaged in deductive (Study 1) and then inductive (Study 2) theorizing and relied upon 35 meta-analyses involving 3327 primary-level studies and 930 349 observations as input for meta-analytic structural equation modeling. Results of our dual deductive-inductive approach revealed an unexpected yet surprisingly consistent explanation for why leadership behaviors affect follower performance. Specifically, leader-member exchange is a mediating mechanism that was empirically determined to be involved in the largest indirect relations between the four major leadership behaviors and follower performance. This result represents a departure from current conceptualizations and points to a common underlying mechanism that parsimoniously explains how leadership behaviors relate to follower performance. Also, results lead to a shift in terms of recommendations for what leaders should focus on to bring about improved follower performance.Why do positive leadership behaviors improve various types of follower performance? This question is critically important for theoretical progression in the leadership domain because if we do not understand why these specific relations occur, we do not have a solid theory (Bacharach, 1989;Dubin, 1978;Sutton & Staw, 1995;Whetten, 1989). Additionally, if we do not clearly understand why leadership behaviors-follower performance relations occur, we will be limited in our ability to provide accurate and actionable recommendations for leaders that will result in the most favorable performance outcomes.While the question above is critically important for theoretical and practical reasons, results to date have led to multiple answers across the various leadership behaviors-follower performance relations. For example, consider one of the most well-researched leadership behavior-follower performance relation: transformational leadership and task performance. An examination of the literature reveals that there are at least eight empirically supported mediators explaining this relation, including self-congruence, empowerment, positive effect, trust, person/job fit, core job characteristics, leader-member exchange (LMX), and work engagement (Aryee, . Similar observations can be made for other often-researched leadership behaviors. For consideration, initiating structure, contingent rewards, and transformational leadership (i.e., the four most frequently studied leadership behaviors), the presence of multiple mediating mechanismsoftentimes more than 10serving as explanations for their relations with follow...

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