Correspondence analysis is a useful tool to uncover the relationships among categorical variables

Nadia Sourial, Christina Wolfson, Bin Zhu, Jacqueline Quail, John Fletcher, Sathya Karunananthan, Karen Bandeen-Roche, François Béland, Howard Bergman

Research output: Contribution to journalArticlepeer-review

208 Scopus citations


Objective: Correspondence analysis (CA) is a multivariate graphical technique designed to explore the relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with the goal of examining associations among these variables. Nevertheless, CA appears to be an underused technique in epidemiology. The objective of this article is to present the utility of CA in an epidemiological context. Study Design and Setting: The theory and interpretation of CA in the case of two and more than two variables are illustrated through two examples. Results: The outcome from CA 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 rowecolumn pairs, which would otherwise not be detected through a pairwise analysis. Conclusion: When the study variables of interest are categorical, CA is an appropriate technique to explore the relationships among variable response categories and can play a complementary role in analyzing epidemiological data.

Original languageEnglish (US)
Pages (from-to)638-646
Number of pages9
JournalJournal of Clinical Epidemiology
Issue number6
StatePublished - Jun 2010


  • Categorical data
  • Correspondence analysis
  • Epidemiology
  • Information dissemination methods
  • Multivariate graphical analysis
  • Relationship

ASJC Scopus subject areas

  • Epidemiology


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