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, etc.). When the independent variable has not been exogenously manipulated, there is a strong possibility that its relationship to a dependent variable will not be correctly estimated, leading to spurious findings. This podcast gives a brief and vivid overview to endogeneity and why it is engendered. Prof. John Antonakis discusses the problems of endogeneity using non-technical language and intuitive explanations; he shows that the observed relationship that is estimated can be very misleading when the independent variable is endogenous.
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