Correspondence analysis is a useful tool to uncover the relationships among categorical variables.
Correspondence analysis is a useful tool to uncover the relationships among categorical variables.
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DOI：
10.1016/j.jclinepi.2009.08.008
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发表时间：
2010-06
影响因子：
7.2
通讯作者：
Bergman, Howard
中科院分区：
文献类型：
--
作者：
Sourial, Nadia;Wolfson, Christina;Zhu, Bin;Quail, Jacqueline;Fletcher, John;Karunananthan, Sathya;Bandeen-Roche, Karen;Beland, Francois;Bergman, Howard
关键词：
对应分析 (CA) 是一种多元图形技术，旨在探索分类变量之间的关系。流行病学家经常收集多个分类变量的数据，目的是检查这些变量之间的关联。然而，尽管CA在这方面很有用，但它在流行病学中似乎并未得到充分利用。本文的目的是展示 CA 在流行病学背景下的实用性。通过两个例子说明了两个变量和两个以上变量情况下CA的理论和解释。对应分析的结果是列联表的行和列的图形显示，该表旨在允许可视化低维空间中变量响应之间的显着关系。这种表示揭示了行列对之间关系的更全局的图景，否则通过成对分析无法检测到这些关系。当感兴趣的研究变量是分类的时，CA 是探索变量响应类别之间关系的适当技术，并且可以在分析流行病学数据中发挥补充作用。
Correspondence Analysis (CA) is a multivariate graphical technique designed to explore relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with to the goal of examining associations amongst these variables. Nevertheless, despite its usefulness in this context, CA appears to be an underused technique in epidemiology. The objective of this paper is to present the utility of CA in an epidemiological context. The theory and interpretation of CA in the case of two variables and more than two variables is illustrated through two examples. The outcome from correspondence analysis is a graphical display of the rows and columns of a contingency table that is designed to permit visualization of the salient relationships among the variable responses in a low-dimensional space. Such a representation reveals a more global picture of the relationships among row-column pairs which would otherwise not be detected through a pairwise analysis. When the study variables of interest are categorical, CA is an appropriate technique to explore relationships amongst variable response categories and can play a complementary role in analyzing epidemiological data.
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DOI：
10.1017/s0305004100013517
发表时间：
1935-01-01
影响因子：
--
作者：
Hirschfeld, H
通讯作者：
Hirschfeld, H
DOI：
10.1093/gerona/61.4.367
发表时间：
2006-04-01
影响因子：
5.1
作者：
Béland, F;Bergman, H;Dallaire, L
通讯作者：
Dallaire, L
影响因子：
3.7
作者：
GOODMAN, LA
通讯作者：
GOODMAN, LA
影响因子：
7.2
作者：
COSTE, J;SPIRA, A;PAOLAGGI, JB
通讯作者：
PAOLAGGI, JB
影响因子：
3.8
作者：
CATTELL, RB
通讯作者：
CATTELL, RB