Mixed-effects models in psychophysiology
Mixed-effects models in psychophysiology
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DOI：
10.1111/1469-8986.3710013
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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
目前的心理生理学方法论政策规定，重复测量设计应使用多变量方差分析(ANOVA)或具有温室-盖泽或Huynh-Feldt校正的重复测量ANOVA进行分析。在关于数据的方差-协方差矩阵的一般假设下，这两种技术都会产生合适的I型En或概率。这份报告介绍了混合效应模型作为分析心理生理学重复测量数据的替代方法。与传统方法相比，混合效应模型具有许多优势：它们更有效地处理丢失的数据，并且更高效、简洁和灵活。我们描述了混合效果建模，并用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.