Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of the methodology, this book brings together the theoretical and methodological aspects of MFA. It also covers principal component analysis, multiple correspondence analysis, factor analysis for mixed data, hierarchical MFA, and more. The book also includes examples of applications and details on how to implement MFA using an R package, with the data and R scripts available online.

Jerome Pages is a professor of statistics at Agrocampus (Rennes, France), where he heads the Laboratory of Applied Mathematics (LMA ).

Principal Component Analysis. Multiple Correspondence Analysis. Factor Analysis for Mixed Data. Weighting Groups of Variables. Comparing Clouds of Partial Individuals. Factors Common to Different Groups of Variables. Comparing Groups of Variables and Indscal Model. Qualitative and Mixed Data. Multiple Factor Analysis and Procrustes Analysis. Hierarchical Multiple Factor Analysis. Matrix Calculus and Euclidean Vector Space. Bibliography.