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Re: st: Reverse Causality calculation methods

From   John Antonakis <>
Subject   Re: st: Reverse Causality calculation methods
Date   Fri, 22 Feb 2013 14:04:14 +0100


There are many different designs you could use, if you have the right data, or if you have gathered the data in the right way. Of course, the participants have not been randomly assigned to do sports, so an omitted cause could be driving both sports and satisfaction, or there could be simultaneity. The easiest (technically speaking) way to deal with this is to have instruments. There is no quick fix here and without knowing the the domain it is not possible to give you ideas about how to do it. This is something you should discuss with your supervisor, normally. For ideas, however, see:

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (submitted). Causality and endogeneity: Problems and solutions. In D.V. Day (Ed.), The Oxford Handbook of Leadership and Organizations.
See also:

For a more advanced treatment see:
Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(4), 69-85.

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6). 1086-1120.

Grant, A. M., & Wall, T. D. (2009). The Neglected Science and Art of Quasi-Experimentation: Why-to, When-to, and How-to Advice for Organizational Researchers. Organizational Research Methods, 12(4), 653-686.

Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business & Economics Statistics, 13(2), 151-161.

Diamond, J. M., & Robinson, J. A. (2010). Natural experiments of history. Cambridge, Mass.: Belknap Press of Harvard University Press.

Shadish, W. R., & Cook, T. D. (1999). Comment-Design Rules: More Steps toward a Complete Theory of Quasi-Experimentation. Statistical Science, 14(3), 294-300.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.



John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Associate Editor
The Leadership Quarterly

On 22.02.2013 12:46, Alexandra Grabbe wrote:
Hello everyone,

I am currently working on my Master's Thesis, analysing the impact of
sporting activities on job satisfaction using panel data over five
Including additional control variables in the model, my regression
identifies Sport as a statistically significant factor influencing
satisfaction at work.
As a further step I would now like to exclude a possible "reverse
causality", such that job satisfaction affects sports instead of vice
Does someone have an idea how I can approach this problem?
(As a side note: the data consists of appr. 36,000 observations)

I have thought of analysing certain individuals of the data set by
looking at the change of their sporting behaviour and their job
satisfaction. But I am not sure of how to do this technically with
Stata. Another option would be to identify instrumental variables
describing sports, but this would be quite difficult and only
approaches the problem to a certain point. Thus, new ideas are
welcome. Please help!

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