%0 Journal Article %1 bell2018understanding %A Bell, Andrew %A Jones, Kelvyn %A Fairbrother, Malcolm %C Switzerland %D 2018 %J Quality & quantity %K centring group means methodology multilevel %N 5 %P 2031--2036 %R 10.1007/s11135-017-0593-5 %T Understanding and misunderstanding group mean centering: a commentary on Kelley et al.'s dangerous practice %U https://pubmed.ncbi.nlm.nih.gov/30147154 %V 52 %X 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.

@article{bell2018understanding, 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.}, added-at = {2024-08-09T09:22:09.000+0200}, address = {Switzerland}, author = {Bell, Andrew and Jones, Kelvyn and Fairbrother, Malcolm}, biburl = {https://www.bibsonomy.org/bibtex/2a1d36471b2d8536cc5d3a43e84a30b06/bastionjersey}, comment = {30147154[pmid] PMC6096905[pmcid]}, description = {Understanding and misunderstanding group mean centering: a commentary on Kelley et al.’s dangerous practice - PMC}, doi = {10.1007/s11135-017-0593-5}, interhash = {c0b182ef8fe397e226f2b0acf57cba0d}, intrahash = {a1d36471b2d8536cc5d3a43e84a30b06}, issn = {00335177}, journal = {Quality & quantity}, keywords = {centring group means methodology multilevel}, number = 5, pages = {2031--2036}, timestamp = {2024-08-09T09:22:09.000+0200}, title = {Understanding and misunderstanding group mean centering: a commentary on Kelley et al.'s dangerous practice}, url = {https://pubmed.ncbi.nlm.nih.gov/30147154}, volume = 52, year = 2018 }