a r t i c l e i n f o a b s t r a c t Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity -which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error -renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

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.

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Most leadership and management researchers ignore one key design and estimation problem rendering parameter estimates uninterpretable: Endogeneity. We discuss the problem of endogeneity in depth and explain conditions that engender it using examples grounded in the leadership literature. We show how consistent causal estimates can be derived from the randomized experiment, where endogeneity is eliminated by experimental design. We then review the reasons why estimates may become biased (i.e., inconsistent) in non-experimental designs and present a number of useful remedies for examining causal relations with nonexperimental data. We write in intuitive terms using nontechnical language to make this chapter accessible to a large audience.

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