Centering or Not Centering in Multilevel Models? The Role of the Group Mean and the Assessment of Group Effects

Abstract

In multilevel regression, centering the model variables produces effects that are different and sometimes unexpected compared with those in traditional regression analysis. In this article, the main contributions in terms of meaning, assumptions, and effects underlying a multilevel centering solution are reviewed, emphasizing advantages and critiques of this approach. In addition, in the spirit of Manski, contextual and correlated effects in a multilevel framework are defined to detect group effects. It is shown that the decision of centering in a multilevel analysis depends on the way the variables are centered, on whether the model has been specified with or without cross-level terms and group means, and on the purposes of the specific analysis.