Title

Mean-centering does not alleviate collinearity problems in moderated multiple regression models

Authors

Authors

R. Echambadi;J. D. Hess

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Mark. Sci.

Keywords

moderated regression; mean-centering; collinearity; EMPIRICAL GENERALIZATIONS; VARIABLES; Business

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 R-2. We also show that the determinants of the cross product matrix X ' X 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.

Journal Title

Marketing Science

Volume

26

Issue/Number

3

Publication Date

1-1-2007

Document Type

Article

Language

English

First Page

438

Last Page

445

WOS Identifier

WOS:000248660600011

ISSN

0732-2399

Share

COinS