Centering Predictor Variables in Three-Level Contextual Models

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31 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)149-163
Number of pages15
JournalMultivariate Behavioral Research
Issue number2
StatePublished - Mar 4 2017


  • Multilevel modeling
  • centering
  • hierarchical linear modeling

ASJC Scopus subject areas

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


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