Mean-Centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models

Raj Echambadi () and James D. Hess ()
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Raj Echambadi: Department of Marketing, College of Business Administration, University of Central Florida, P.O. Box 161400, Orlando, Florida 32816-1400
James D. Hess: Department of Marketing and Entrepreneurship, C.T. Bauer College of Business, 375H Melcher Hall, University of Houston, Houston, Texas 77204

Marketing Science, 2007, vol. 26, issue 3, 438-445

Abstract: The cross-product term in moderated regression may be collinear with its constituent parts, making it difficult to detect main, simple, and interaction effects. The literature shows that mean-centering can reduce the covariance between the linear and the interaction terms, thereby suggesting that it reduces collinearity. We analytically prove that mean-centering neither changes the computational precision of parameters, the sampling accuracy of main effects, simple effects, interaction effects, nor the 2 . We also show that the determinants of the cross product matrix are identical for uncentered and mean-centered data, so the collinearity problem in the moderated regression is unchanged by mean-centering. Many empirical marketing researchers commonly mean-center their moderated regression data hoping that this will improve the precision of estimates from ill conditioned, collinear data, but unfortunately, this hope is futile. Therefore, researchers using moderated regression models should not mean-center in a specious attempt to mitigate collinearity between the linear and the interaction terms. Of course, researchers may wish to mean-center for interpretive purposes and other reasons.

Keywords: moderated regression; mean-centering; collinearity (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (86)

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