title: Correspondence analysis is a useful tool to uncover the relationships among categorical variables.
abstract: 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.
  
  
  METHODS
  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 row-column pairs, which would otherwise not be detected through a pairwise analysis.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Solidage Research Group, Centre for Clinical Epidemiology and Community Studies, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Quebec, Canada.
authors.forename: Nadia
authors.initials: N
authors.lastname: Sourial
authors.forename: Christina
authors.initials: C
authors.lastname: Wolfson
authors.forename: Bin
authors.initials: B
authors.lastname: Zhu
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Quail
authors.forename: John
authors.initials: J
authors.lastname: Fletcher
authors.forename: Sathya
authors.initials: S
authors.lastname: Karunananthan
authors.forename: Karen
authors.initials: K
authors.lastname: Bandeen-Roche
authors.forename: François
authors.initials: F
authors.lastname: Béland
authors.forename: Howard
authors.initials: H
authors.lastname: Bergman
mesh_terms: D000465:Algorithms; D032882:Comprehension; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical