Centering Predictor Variables in Cross-Sectional Multilevel Models

A New Look at an Old Issue

Craig K. Enders, Davood Tofighi

Research output: Contribution to journalArticle

1638 Citations (Scopus)

Abstract

Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in the context of 2-level MLMs. The authors begin with a basic overview of centering and explore the differences between grand and group mean centering in the context of some prototypical research questions. Empirical analyses of artificial data sets are used to illustrate key points throughout. The article provides a number of practical recommendations designed to facilitate centering decisions in MLM applications.

Original languageEnglish (US)
Pages (from-to)121-138
Number of pages18
JournalPsychological Methods
Volume12
Issue number2
DOIs
StatePublished - Jun 2007

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Keywords

  • centering
  • grand mean centering
  • group mean centering
  • hierarchical linear models
  • multilevel models

ASJC Scopus subject areas

  • Psychology(all)

Cite this

Centering Predictor Variables in Cross-Sectional Multilevel Models : A New Look at an Old Issue. / Enders, Craig K.; Tofighi, Davood.

In: Psychological Methods, Vol. 12, No. 2, 06.2007, p. 121-138.

Research output: Contribution to journalArticle

Enders, Craig K. ; Tofighi, Davood. / Centering Predictor Variables in Cross-Sectional Multilevel Models : A New Look at an Old Issue. In: Psychological Methods. 2007 ; Vol. 12, No. 2. pp. 121-138.
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