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).
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This is an extremely helpful (and entertaining) introduction to endogeneity that will be useful for scholars operating in many disciplines.
Very engaging and elucidating video. The graphics were a wonderful extension of your explanation making the concepts very clear.
POLI 210 Mccill whats good fam
Thank you
Thanks Dr. Antonakis. Would be amazing to see a follow up to discuss issues that you briefly touched on, such as endogeneity in HLM.
I wish more people (including myself) truly and completely understand the content you are delivering.
Link to his paper for further reading:
http://datascienceassn.org/sites/default/files/On%20making%20causal%20claims%20A%20review%20and%20recommendations.pdf
Dear professor, while your paper is great, you use notation β1 = inconsistent. This has created some confusion, because a parameter is not consistent or inconsistent. β1 is referring to the estimator, which is an odd notation. Thanks anyway.
Great explanation of endogeneity concept; thank you professor
Great explanation! (Except for talking about Swiss Francs with Euros bills falling in the backgroud. We are not in the Euro zone, and proud not to be!)
Amazing explanation. I knew nothing about it, now i understood it very well. Thank you
very nice….explained in the most easiest way.thanks sir
love how you explained randomization tho
prof i disagree with you saying there is no corellation. there is corellation but there is no causation.
Hello Mister Yianni!
Hello, how can i use 2sls in SPSS? Please help!!
very well explained. thx sir
Helpful! Thank you!
Brilliant and intuitve!
Hats off to you professor! You made the concept very simple. Thanks very much.
Thanks. It is very helpful.
Instructive, thanks!
Dear Professor! Thank you very much indeed for sharing this great video 🙂 I am wondering if you could share a video on how to apply the concepts you have discussed in this video, preferably from the paper you mentioned in this video. The paper is a good read and useful. I feel truly grateful to you and would appreciate your kind response 🙂
Thank you – this was great
I was able to understand somewhat but it is a little out of my scope. 🙂