Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

• Published in: Neuron (Cambridge, Mass.) Vol. 110; no. 1; pp. 21 - 35
• Main Authors: Yu, Zhaoxia, Guindani, Michele, Grieco, Steven F., Chen, Lujia, Holmes, Todd C., Xu, Xiangmin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 05.01.2022
• Subjects: Analysis of Variance; Bayesian analysis; clustered data; generalized linear mixed-effects model; linear mixed-effects model; Linear Models; linear regression model; Models, Statistical; Neurosciences; repeated measures; Reproducibility of Results; Research Design; clustered data; generalized linear mixed-effects model; linear mixed-effects model; linear regression model; Bayesian analysis; repeated measures
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2021.10.030

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings. In this Primer, Yu et al. introduce linear and generalized mixed-effects models for improved statistical analysis in neuroscience research and provide clear instruction on how to recognize when they are needed and how to apply them.