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

Andrew Bell (), Kelvyn Jones and Malcolm Fairbrother
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Andrew Bell: Sheffield Methods Institute, University of Sheffield
Kelvyn Jones: University of Bristol
Malcolm Fairbrother: Umeå University

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 5, No 3, 2036 pages

Abstract: Abstract 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.

Keywords: Multilevel models; Random effects; Group-mean-centering; Mundlak; Fixed effects (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11135-017-0593-5

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