The John Antonakis"s podcast

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

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

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

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.

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.