重复措施设计
方差分析
广义线性混合模型
混合模型
考试(生物学)
统计假设检验
心理学
线性模型
计算机科学
数据科学
认知心理学
机器学习
统计
数学
生物
古生物学
作者
Zhaoxia Yu,Michele Guindani,Steven F. Grieco,Lujia Chen,Todd C. Holmes,Xiangmin Xu
出处
期刊:Neuron
[Elsevier]
日期:2021-11-15
卷期号:110 (1): 21-35
被引量:38
标识
DOI:10.1016/j.neuron.2021.10.030
摘要
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.
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