Categorical variables are common in the biomedical field, and many descriptive methods have been proposed for revealing intrinsic patterns in data. Correspondence Analysis is an especially useful method for categorical data analysis of large contingency tables. Although numerous studies have been published on this method, most Portuguese-language articles have failed to explore its full potential, focusing only on graphical interpretation. The current paper reviews the method, showing that graphical analysis can be enriched by the right statistics. The article presents the mathematical basis for correspondence analysis and its most frequently used statistics. The procedure has shown that such statistics enrich symmetric map evaluation, that a low relative frequency category can be represented by supplementary category points, and that inertia contributions are highly related to residual analysis of contingency tables, not easily visualized by symmetric maps. Correspondence Analysis has proven advantageous when compared to principal components analysis.
Multivariate Analysis; Health Care Quality Indicators; Statistical Data Interpretation
