Centering predictor variables in cross-sectional multilevel models: a new look at an old issue

Psychol Methods. 2007 Jun;12(2):121-38. doi: 10.1037/1082-989X.12.2.121.


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.

MeSH terms

  • Cluster Analysis*
  • Cross-Sectional Studies
  • Data Collection / statistics & numerical data*
  • Humans
  • Least-Squares Analysis
  • Linear Models
  • Longitudinal Studies
  • Models, Statistical*
  • Psychology, Experimental / statistics & numerical data*