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 when the independent variable is endogenous--which is also possible in experimental designs (when the mediator is endogenous)--the observed relationship that is estimated can be very misleading. Prof. Antonakis demonstrates how the problem of endogeneity can be solved using procedures borrowed from econometrics (i.e., two-stage least square regression estimator).