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.
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.
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