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心理生理学重复测量研究设计的多层模型:生长曲线建模简介。

Multilevel models for repeated measures research designs in psychophysiology: an introduction to growth curve modeling.

作者信息

Kristjansson Sean D, Kircher John C, Webb Andrea K

机构信息

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63108, USA.

出版信息

Psychophysiology. 2007 Sep;44(5):728-36. doi: 10.1111/j.1469-8986.2007.00544.x. Epub 2007 Jun 26.

Abstract

Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.

摘要

心理生理学家经常使用重复测量方差分析(RMANOVA)和多变量方差分析(MANOVA)来分析在重复测量研究设计中收集的数据。方差分析和多变量方差分析是侧重于组均值的通则性方法。更新的多层次建模技术比方差分析提供了更多信息,因为它们能够描述组水平(通则性)和个体水平(独特性)的效应,从而更全面地理解所研究的现象。本文旨在为应用研究人员介绍生长曲线建模。文中定义了一种生长模型,该模型可用于单组和混合重复测量设计,以替代重复测量方差分析和多变量方差分析。该模型经过扩展,用于检验和控制生理活动基线水平对刺激特异性反应的影响。文中还讨论了生长曲线建模在实际应用、概念和统计方面的优势。

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