The role of correspondence analysis in medical research - Žlahtič, Bojan; Kokol, Peter; Blažun Vošner, Helena; Završnik, Jernej (Frontiers in Public Health, 2024)
Correspondence Analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analysing either two or multi way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in in easy to interpret “bi–plots” where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated, than items located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professional in finding answers on many important questions related to health, well-being, quality of life and similar in simpler but a more informal way than by using more complex statistical or machine learning approaches. In that way it can be used for imension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effect or pattern detection.