Mean centering helps alleviate “micro” but not “macro” multicollinearity

Dawn Iacobucci*, Matthew J. Schneider, Deidre L. Popovich, Georgios A. Bakamitsos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

172 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1308-1317
Number of pages10
JournalBehavior Research Methods
Issue number4
StatePublished - Dec 1 2016


  • Interactions in regression
  • Mean centering
  • Moderated multiple regressions
  • Multicollinearity

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)


Dive into the research topics of 'Mean centering helps alleviate “micro” but not “macro” multicollinearity'. Together they form a unique fingerprint.

Cite this