Note: Sadly, Boas passed away before this article could be completed. In consultation with the editorial team at the Annual Reviews, we added Boas posthumously in the author list. Boas significantly contributed to what we planned to write, he extensively commented on the coding procedure we used, and we had several discussions with him about the content and direction of the article. We thank Laurent Lehmann, Thomas von Ungern-Sternberg, and Christian Zehnder for helpful comments they provided us during the development of this manuscript. We are also very grateful to Manon Jaquerod and Sirio Lonati for their assistance in coding the articles.

Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by including cluster means of the Level 1 explanatory variables as controls; we explain this point conceptually and with a large-scale simulation. We further show why the common practice of centering the predictor variables is mostly unnecessary. Moreover, to examine the state of the science, we reviewed 204 randomly drawn articles from macro and micro organizational science and applied psychology journals, finding that only 106 articles—with a slightly higher proportion from macro-oriented fields—properly deal with the random effects assumption. Alarmingly, most models also failed on the usual exogeneity requirement of the regressors, leaving only 25 mostly macro-level articles that potentially reported trustworthy multilevel estimates. We offer a set of practical recommendations for researchers to model multilevel data appropriately.

We examined drivers of article citations using 776 articles that were published from 1990-2012 in a broad-based and high-impact social sciences journal, The Leadership Quarterly. These articles had 1,191 unique authors having published and received in total (at the time of their most recent article published in our dataset) 16,817 articles and 284,777 citations, respectively. Our models explained 66.6% of the variance in citations and showed that quantitative, review, method, and theory articles were significantly more cited than were qualitative articles or agentbased simulations. As concerns quantitative articles, which constituted the majority of the sample, our model explained 80.3% of the variance in citations; some methods (e.g., use of SEM) and designs (e.g., meta-analysis), as well as theoretical approaches (e.g., use of transformational, charismatic, or visionary type-leadership theories) predicted higher article citations. Regarding the statistical conclusion validity of quantitative articles, articles having endogeneity threats received significantly fewer citations than did those using a more robust design or an estimation procedure that ensured correct causal estimation. We make several general recommendations on how to improve research practice and article citations.

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Borrowed from organizational psychology, the concept of transformational leadership has now been applied to a sport context for a decade. Our review covers and critically discusses empirical articles published on this growing topic. However, because the majority of studies used cross-sectional designs and single-source questionnaires to tap what has been a fuzzy construct, current theoretical and methodological issues impede understanding of whether transformational leadership matters for sport outcomes. To make a difference to applied practice and policy, the transformational leadership construct requires a refined definition and stronger empirical tests allowing for robust causal inference. We highlight avenues for advancing research on transformational leadership in the sport context.

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