
Contents
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What Is Causality? What Is Causality?
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Endogeneity: Two Inconvenient Demonstrations Endogeneity: Two Inconvenient Demonstrations
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Counterfactuals and the Randomized Experiment Counterfactuals and the Randomized Experiment
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The Counterfactual Argument The Counterfactual Argument
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The Pitfalls of Nonexperimental Research The Pitfalls of Nonexperimental Research
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Omitting a Regressor Omitting a Regressor
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Measurement Error (Errors-in-Variables) Measurement Error (Errors-in-Variables)
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Common Source, Common Method Variance Common Source, Common Method Variance
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Omitting Fixed Effects Omitting Fixed Effects
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Omitting Selection Omitting Selection
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Simultaneity Simultaneity
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Consistency of Inference Consistency of Inference
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Quasi-Experimental and Structural Equation Methods Quasi-Experimental and Structural Equation Methods
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Two-Stage Least Squares Estimation Two-Stage Least Squares Estimation
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Two-Stage Least Squares in Practice: An Example Two-Stage Least Squares in Practice: An Example
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A Note on Overidentification A Note on Overidentification
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Other Methods for Inferring Causality Other Methods for Inferring Causality
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Propensity Score Analysis Propensity Score Analysis
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Selection Models (Heckman Models) Selection Models (Heckman Models)
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Regression Discontinuity Models Regression Discontinuity Models
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Difference-in-Differences Models Difference-in-Differences Models
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Conclusions Conclusions
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Future Directions Future Directions
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References References
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6 Causality and Endogeneity: Problems and Solutions
Get accessJohn Antonakis, Faculty of Business and Economics, University of Lausanne
Samuel Bendahan, Faculty of Business and Economics, University of Lausanne
Philippe Jacquart, The Wharton School, University of Pennsylvania
Rafael Lalive, Faculty of Business and Economics, University of Lausanne
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Published:13 January 2014
Cite
Abstract
Unfortunately, most researchers in leadership studies (and management in general) ignore one key design and estimation problem rendering parameter estimates uninterpretable: endogeneity. This chapter discusses the problem of endogeneity in depth and explains conditions that engender it. It shows how consistent causal estimates can be derived from the randomized experiment, in which endogeneity is eliminated by experimental design. It then reviews the reasons why estimates may become biased (i.e., inconsistent) in nonexperimental designs and presents a number of useful remedies for examining causal relations with nonexperimental data. This chapter is written in intuitive terms using nontechnical language to make it accessible to a large audience, irrespective of discipline.
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