Multiple Correspondence Analysis (MCA) is an exploratory multivariate technique designed to analyze and visualize relationships among several categorical variables. It can be seen as the extension of Correspondence Analysis (CA), which is limited to two categorical variables, to the case of more than two variables.
Category Archives: Multivariate analysis
Correspondence Analysis (CA) in Excel
Correspondence Analysis (CA) is an exploratory multivariate statistical technique designed to analyze and visualize relationships in categorical data. It is most often applied to contingency tables—tables that cross-classify observations into categories—making it particularly useful for survey data, market research, linguistics, ecology, and the social sciences.
Principal Component Analysis (PCA)
Principal component analysis (PCA) is a dimensionality reduction method that transforms a large set of variables into a smaller one but still containing most of the information in the large set.