Citation

Bell A, Jones K, Fairbrother M. Qual. Quant. 2018; 52(5): 2031-2036.

Affiliation

Department of Sociology, Umeå University, 901 87 Umeå, Sweden.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11135-017-0593-5

PMID

30147154

PMCID

PMC6096905

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.

Language: en

Keywords

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