Centering Predictor Variables in Three-Level Contextual Models

Ahnalee M. Brincks, Craig K. Enders, Maria Llabre, Rebecca Shearer, Guillermo J Prado, Daniel J Feaster

Research output: Contribution to journalArticle

20 Scopus citations


Hierarchical data are becoming increasingly complex, often involving more than two levels. Centering decisions in multilevel models are closely tied to substantive hypotheses and require researchers to be clear and cautious about their choices. This study investigated the implications of group mean centering (i.e., centering within context; CWC) and grand mean centering (CGM) of predictor variables in three-level contextual models. The goals were to (a) determine equivalencies in the means and variances across the centering options and (b) use the algebraic relationships between the centering choices to clarify the interpretation of the estimated parameters. We provide recommendations to assist the researcher in making centering decisions for analysis of three-level contextual models

Original languageEnglish (US)
Pages (from-to)0
Number of pages1
JournalMultivariate Behavioral Research
StateAccepted/In press - Dec 6 2016


  • centering
  • hierarchical linear modeling
  • Multilevel modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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