Understanding and misunderstanding group mean centering: a commentary on Kelley et al.’s dangerous practice

Andrew Bell*, Kelvyn Jones, Malcolm Fairbrother

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)
282 Downloads (Pure)


Kelley et al. argue that group-mean-centering covariates in multilevel models is dangerous, since—they claim—it generates results that are biased and misleading. We argue instead that what is dangerous is Kelley et al.’s unjustified assault on a simple statistical procedure that is enormously helpful, if not vital, in analyses of multilevel data. Kelley et al.’s arguments appear to be based on a faulty algebraic operation, and on a simplistic argument that parameter estimates from models with mean-centered covariates must be wrong merely because they are different than those from models with uncentered covariates. They also fail to explain why researchers should dispense with mean-centering when it is central to the estimation of fixed effects models—a common alternative approach to the analysis of clustered data, albeit one increasingly incorporated within a random effects framework. Group-mean-centering is, in short, no more dangerous than any other statistical procedure, and should remain a normal part of multilevel data analyses where it can be judiciously employed to good effect.

Original languageEnglish
Pages (from-to)2031-2036
Number of pages6
JournalQuality and Quantity
Issue number5
Early online date7 Nov 2017
Publication statusPublished - Sep 2018


  • Fixed effects
  • Group-mean-centering
  • Multilevel models
  • Mundlak
  • Random effects

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