Conducting and interpreting canonical correlation analysis in personality research: A user-friendly primer
Conducting and interpreting canonical correlation analysis in personality research: A user-friendly primer
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DOI：
10.1207/s15327752jpa8401_09
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发表时间：
2005-02-01
影响因子：
3.4
通讯作者：
Henson, RK
中科院分区：
文献类型：
--
作者：
Sherry, A;Henson, RK
这篇文章的目的是减少潜在的统计障碍，为应用行为科学家和人格研究人员打开典型相关分析（CCA）的大门。选择CCA进行讨论，因为它代表了一般线性模型（GLM）的最高水平，并且可以相当容易地概念化为与更广泛理解的Pearson r相关系数密切相关的方法。对CCA的理解可以导致对GLM中其他单变量和多变量方法的更全面的理解。我们试图用基本的语言来演示CCA，只有在必要时才使用技术术语来理解和使用该方法。我们提出了一个完整的例子，CCA分析使用SPSS（11.0版）与个性数据。
The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely understood Pearson r correlation coefficient. An understanding of CCA can lead to a more global appreciation of other univariate and multivariate methods in the GLM. We attempt to demonstrate CCA with basic language, using technical terminology only when necessary for understanding and use of the method. We present an entire example of a CCA analysis using SPSS (Version 11.0) with personality data.