Correspondence analysis is a useful tool to uncover the relationships among categorical variables.
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.
Sourial
Nadia
N
Solidage Research Group, Centre for Clinical Epidemiology and Community Studies, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Quebec, Canada.
Wolfson
Christina
C
Zhu
Bin
B
Quail
Jacqueline
J
Fletcher
John
J
Karunananthan
Sathya
S
Bandeen-Roche
Karen
K
Béland
François
F
Bergman
Howard
H
D000465:Algorithms; D032882:Comprehension; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical