This book provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right; no prior knowledge of Correspondence Analysis (CA) is needed. The presentation is practically oriented and with the needs of research in mind: gathering relevant data, formulating questions of interest, and linking statistical interpretation to geometric representations. The procedures are presented in detail using a real example, stressing the unique capacity of MCA to handle full scale research studies.
This book provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right; no prior knowledge of Correspondence Analysis (CA) is needed. The presentation is practically oriented and with the needs of research in mind: gathering relevant data, formulating questions of interest, and linking statistical interpretation to geometric representations. The procedures are presented in detail using a real example, stressing the unique capacity of MCA to handle full scale research studies.
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.
Inhaltsangabe
About the Authors Series Editor¿s Introduction Acknowledgments 1. Introduction 2. The Geometry of a Cloud of Points 3. The Method of Multiple Correspondence Analysis 4. Structured Data Analysis 5. Inductive Data Analysis 6. Full-Scale Research Studies Appendix References Index
About the Authors Series Editor¿s Introduction Acknowledgments 1. Introduction 2. The Geometry of a Cloud of Points 3. The Method of Multiple Correspondence Analysis 4. Structured Data Analysis 5. Inductive Data Analysis 6. Full-Scale Research Studies Appendix References Index
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