Mean centering helps alleviate “micro” but not “macro” multicollinearity
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- 作者：Dawn Iacobucci ; Matthew J. Schneider ; Deidre L. Popovich…
- 关键词：Mean centering ; Multicollinearity ; Moderated multiple regressions ; Interactions in regression
- 刊名：Behavior Research Methods
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：48
- 期：4
- 页码：1308-1317
- 全文大小：461 KB
- 刊物主题：Cognitive Psychology;
- 出版者：Springer US
- ISSN：1554-3528
- 卷排序：48
文摘
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.