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Re: st: A layman question on model building

From   John Antonakis <>
Subject   Re: st: A layman question on model building
Date   Thu, 07 Mar 2013 08:22:46 +0100


If you have a sufficiently large sample size and the regressors of interest are significant predictors, then it is best to leave in the controls; they do not harm but help consistency (even if only a tad). What suffers is efficiency (standard errors) and this is amplified in small sample size conditions. I would (mostly) always err on the side of caution and include the controls. A couple of things to do before considering dropping are: (a) to do a Wald test to test whether the controls are simultaneously different from zero; (b) to do a Hausman test comparing the consistent and efficient estimator or a Chow test for the common regressors. See the first series of discussions (eq. 2-5c to see that consistency is never harmed even if a omitted regressor is not a significant predictor: Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2010. On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6): 1086-1120.



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 07.03.2013 08:07, James Bernard wrote:
Hi all,

I have a question which may sound too basic, but I wonder if anyone could help:

We often add control variables that turn out to be insignificant. Does
that mean that I can remove that variable form my model without being
concerned with omitted variable bias?

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