Advertisement
Journal Home
Search for

Articles in Press

Return to articles in press list

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

Nadia Souriala, Christina WolfsonabcCorresponding Author Informationemail address, Bin Zhub, Jacqueline Quailbc, John Fletchera, Sathya Karunananthana, Karen Bandeen-Roched, François Bélandaef, Howard Bergmanaef

Accepted 6 August 2009. published online 09 November 2009.
Corrected Proof

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.

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 row–column 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.

a Solidage Research Group, Centre for Clinical Epidemiology and Community Studies, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada

b Division of Clinical Epidemiology, McGill University Health Centre, 1025 Pine Avenue West, Suite P2.028, Montreal, Quebec, Canada

c Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

d Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

e Department of Health Administration, Université de Montréal, Montreal, Quebec, Canada

f Division of Geriatric Medicine, Department of Medicine, Jewish General Hospital, McGill University, Montreal, Quebec, Canada

Corresponding Author InformationCorresponding author. Tel.: +514-934-1934 ext. 44739; fax: +514-934-4458.

PII: S0895-4356(09)00237-6

doi:10.1016/j.jclinepi.2009.08.008

Advertisement