Re: st: Reverse Causality calculation methods

[John Antonakis <John.Antonakis@unil.ch>]

Hi:

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. 
http://www.hec.unil.ch/jantonakis/Causality_and_endogeneity_final.pdf
See also: http://www.youtube.com/watch?v=dLuTjoYmfXs

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. 
http://www.hec.unil.ch/jantonakis/Causal_Claims.pdf

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.

HTH,
J.



__________________________________________

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
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

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
> years.
> 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
> versa.
> 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!
>
> Alexandra
> *
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> *   http://www.ats.ucla.edu/stat/stata/

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