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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Raj Echambadi & James D. Hess, 2007. "Mean-Centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models," Marketing Science, INFORMS, vol. 26(3), pages 438-445, 05-06.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:3:p:438-445
    DOI: 10.1287/mksc.1060.0263
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    References listed on IDEAS

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    1. Rajdeep Grewal & Joseph A. Cote & Hans Baumgartner, 2004. "Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing," Marketing Science, INFORMS, vol. 23(4), pages 519-529, June.
    2. Praveen K. Kopalle & Donald R. Lehmann, 2006. "Setting Quality Expectations When Entering a Market: What Should the Promise Be?," Marketing Science, INFORMS, vol. 25(1), pages 8-24, 01-02.
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