The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis.

Background: Multilevel data analysis is a powerful analytical tool. Properly applying the models and correctly interpreting the findings are two interrelated general issues in using multilevel modeling (MLM). There are two specific issues when using MLM: (a) separating the individual-level effects of a predictor variable from its contextual effects and (b) centering first-level predictor variables.