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

TLDR

Appropriately centering Level 1 predictors is vital to interpreting intercept and slope parameters in multilevel models, yet the issue remains widely misunderstood. The article aims to give a detailed overview of grand‑mean and group‑mean centering in 2‑level MLMs. The authors review centering concepts and illustrate differences between grand and group‑mean centering with prototypical research questions and empirical analyses of artificial data sets. The paper offers practical recommendations to guide centering decisions in MLM applications.

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.