Multiple Correspondence Analysis

Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind.

Key Features

• Readers learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations.
• They also learn how to perform structured data analysis and to draw inferential conclusions from MCA.
• The text uses real examples to help explain concepts.
• The authors stress the distinctive capacity of MCA to handle full-scale research studies.

This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.

Brigitte Le Roux has a doctorate in mathematics (specialty Statistics) (Faculty of Sciences in Paris, 1970) and holds an HDR in Applied Mathematics (University of Paris Dauphine, 2000). She is a member of the laboratory MAP5 (Applied Mathematics Paris 5) of the University Paris Descartes. His research focuses on the geometric data analysis and its applications in social sciences and in particular to the analysis of questionnaires.

Brigitte Le Roux is a member of the editorial board of the journal Mathematics and Humanities and the Acts of Research in Social Sciences.