Correspondence analysis is a useful tool to uncover the relationships among categorical variables
By N. Sourial, C. Wolfson, B. Zhu, J. Quail, John D. Fletcher, S. Karunananthan, Karen Bandeen-Roche, F. Béland, Howard Bergman. Journal of Clinical Epidemiology, 2009
430 citations recorded by the source corpus.
Objective Correspondence Analysis (CA) is a multivariate graphical technique designed to explore relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with to the goal of examining associations amongst these variables. Nevertheless, despite its usefulness in this context, CA appears to be an underused technique in epidemiology. The objective of this paper 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 variables and more than two variables is illustrated through two examples. Results The outcome from correspondence analysis 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 relationships amongst variable response categories and can play a complementary role in analyzing epidemiological data.
Subfields: Computer Networks and Communications, Experimental and Cognitive Psychology, Food Science
View Paper: https://www.semanticscholar.org/paper/ba8d4256536d5a091177a1859fd1a905b961e584
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Citations in the corpus
- Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries (2019, methodology): “The use of statistics and charts specific to that method provides the (1) Oeconomia Copernicana, 10(4), 743–772 753 researcher with easy, intuitive reasoning on the relationships between the analysed categories of variables (Sourial et al., 2010, pp. 638–646).”