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

    Authors

    R. Echambadi;J. D. Hess

    Comments

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    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

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