Endogeneity: An inconvenient truth (for researchers)
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Description
It is well known that endogeneity leads to inconsistent estimates. Unfortunately, many researchers working outside of economics are not aware of the problem of endogeneity and how to deal with it. Prof. John Antonakis shows how the two-stage least squares (2SLS) estimator recovers causal estimates in the presence of endogeneity (which includes the problem of common-method variance). He also shows that endogeneity can even be prevalent in experimental designs, when researchers estimate mediation models; that is, where the causal effect of an exogenous variable on a dependent variable is mediated by an endogenous variable (or a manipulation check).
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A key assumption of regression analysis (or structural equation modeling) is that the modeled independent variables are not endogenous. Yet, the problems of endogeneity are not well known to researchers working in many social sciences disciplines (e.g., management, applied psychology, sociology,...
Published 11/01/11
A key assumption of regression analysis (or structural equation modeling) is that the modeled independent variables are not endogenous. Yet, the problems of endogeneity are not well known to researchers working in many social sciences disciplines (e.g., management, applied psychology, sociology,...
Published 11/01/11
Ulrich Hoffrage questionne John Antonakis sur la recherche qu'il a conduite avec Olaf Dalgas et qui donne lieu à une publication dans la revue Science de février 2009.
Published 02/27/09