Mean centering is an additive transformation of a continuous variable. It is often used in moderated multiple regression models, in regression models with polynomial terms, in moderated structural equation models, or in multilevel models. Mean centering has been offered as a remedy for problems of collinearity in moderated multiple regression models or in polynomial regression models. But, mean centering does not reduce essential collinearity due to substantial relationships between variables. However, for the sake of interpretability of results it is recommended that researchers center predictor variables when their variables do not have meaningful zero points. Mean centering in multilevel models is more complex, as two types of mean centering are available.