Centering categorical predictors in multilevel models: Best practices and interpretation.

Area of Science:

• Statistics
• Psychology
• Social Sciences

Background:

• Centering is vital in multilevel modeling (MLM) for parameter estimation and interpretation.
• Existing literature primarily addresses continuous predictors, neglecting categorical ones.
• Categorical predictors are common, yet often centered inconsistently or not at all in applied research.

Approach:

• This tutorial clarifies the necessity and methods for centering categorical predictors in MLM.
• It provides algebraic derivations to explain interpretation of effects under different coding schemes (dummy, contrast, effect).
• An empirical example demonstrates the practical application of these techniques.

Key Points:

• Uncentered coding conflates within- and between-cluster effects for multicategorical predictors.
• Appropriate centering techniques isolate and clarify these level-specific effects.
• Understanding centered effects is essential for accurate interpretation of regression coefficients.

Conclusions:

• Proper centering of categorical predictors in MLM is essential for valid statistical inference.
• The findings offer guidance for interpreting multilevel regression coefficients involving categorical variables.
• Implications extend to control variables, interaction terms, and multilevel latent variable models.