Mixed-effects models in psychophysiology
Mixed-effects models in psychophysiology
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DOI：
10.1017/s0048577200980648
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发表时间：
2000-01-01
期刊：
影响因子：
3.7
通讯作者：
Heitjan, DF
中科院分区：
文献类型：
--
作者：
Bagiella, E;Sloan, RP;Heitjan, DF
心理生理学的现行方法政策规定，重复测量设计应使用多变量方差分析（ANOVA）或重复测量ANOVA与Greenhouse-Geisser或Huynh-Feldt校正进行分析。这两种技术导致适当的I型en或概率的一般假设下的方差-协方差矩阵的数据。本报告介绍了混合效应模型作为一种替代程序的重复测量数据的分析心理生理学。与传统方法相比，混合效应模型具有许多优点：它们更有效地处理缺失数据，更高效，更简洁，更灵活。我们描述了混合效应建模，并通过一个简单的例子说明了它的适用性。
The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I en or probabilities under general assumptions about the variance-covariance matrix of the data. This report introduces mixed-effects models as an alternative procedure for the analysis of repeated-measures data in Psychophysiology. Mixed-effects models have many advantages over the traditional methods: They handle missing data more effectively and are more efficient, parsimonious, and flexible. We described mixed-effects modeling and illustrated its applicability with a simple example.