Neuron. 2021 Nov 9:S0896-6273(21)00845-X. doi: 10.1016/j.neuron.2021.10.030. Online ahead of print.
ABSTRACT
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.
PMID:34784504 | DOI:10.1016/j.neuron.2021.10.030
