The anova to mixed model transition

Matthieu P Boisgontier; Boris Cheval

doi:10.1016/j.neubiorev.2016.05.034

The anova to mixed model transition

Neurosci Biobehav Rev. 2016 Sep:68:1004-1005. doi: 10.1016/j.neubiorev.2016.05.034. Epub 2016 May 27.

Authors

Matthieu P Boisgontier¹, Boris Cheval²

Affiliations

¹ Movement Control and Neuroplasticity Research Group, Department of Kinesiology, Biomedical Sciences Group, KU Leuven, Leuven, Belgium. Electronic address: matthieu.boisgontier@faber.kuleuven.be.
² Methodology and Data Analysis Research Group, Faculty of Psychology and Educational Sciences (FAPSE), Geneva University, Geneva, Switzerland.

PMID: 27241200
DOI: 10.1016/j.neubiorev.2016.05.034

Abstract

A transition towards mixed models is underway in science. This transition started up because the requirements for using analyses of variances are often not met and mixed models clearly provide a better framework. Neuroscientists have been slower than others in changing their statistical habits and are now urged to act.

Keywords: Analysis of variance; Linear mixed-effects models; Neuroscience; Statistics.

MeSH terms

Analysis of Variance
Models, Statistical*