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Mean centering helps alleviate "micro" but not "macro" multicollinearity
Journal article   Open access   Peer reviewed

Mean centering helps alleviate "micro" but not "macro" multicollinearity

Dawn Iacobucci, Matthew J. Schneider, Deidre L. Popovich and Georgios A. Bakamitsos
Behavior research methods, v 48(4), pp 1308-1317
01 Dec 2016
DOI: https://doi.org/10.3758/s13428-015-0624-x
PMID: 26148824
url
https://doi.org/10.3758/s13428-015-0624-xView
Published, Version of Record (VoR) Open

Abstract

Psychology Psychology, Experimental Psychology, Mathematical Social Sciences ESI Highly Cited Paper (Incites)
There seems to be confusion among researchers regarding whether it is good practice to center variables at their means prior to calculating a product term to estimate an interaction in a multiple regression model. Many researchers use mean centered variables because they believe it's the thing to do or because reviewers ask them to, without quite understanding why. Adding to the confusion is the fact that there is also a perspective in the literature that mean centering does not reduce multicollinearity. In this article, we clarify the issues and reconcile the discrepancy. We distinguish between "micro" and "macro" definitions of multicollinearity and show how both sides of such a debate can be correct. To do so, we use proofs, an illustrative dataset, and a Monte Carlo simulation to show the precise effects of mean centering on both individual correlation coefficients as well as overall model indices. We hope to contribute to the literature by clarifying the issues, reconciling the two perspectives, and quelling the current confusion regarding whether and how mean centering can be a useful practice.

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291 citations in Web of Science
285 citations in Scopus

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Highly Cited Paper 
Collaboration types
Domestic collaboration
Web of Science research areas
Psychology, Experimental
Psychology, Mathematical
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