Multiple Correspondence Analysis and Related Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Michael Greenacre, Jorg Blasius, Shizuhiko Nishisato, Elena Abascal Fernandez, Carolyn Anderson, Matthias Angermeyer, Monica Becue Bertaut, Antonio Blaquez Zaballos, Stephanie Bougeard, Henri Caussinus, Jan de Leeuw, M. Purificacion Galindo Villardon, Ignacio Garcia Lautre, Beatriz Goitisolo, John Gower, Patrick J. F. Groenen, Mohamed Hanafi, Willem Heiser, Alex Koning, Pieter Kroonenberg, Isabel Calvo, Ludovic Lebart, Herbert Matschinger, Oleg Nenadic, Ndeye Niang, Hicham Nocairi, Jerome Pages, Rafael Pardo, Mostafa Qannari, Henry Rouanet, Anne Ruiz-Gazen, Gilbert Saporta, Yoshio Takane, Victor Thiessen, Anna Torres Lacomba, Wijbrandt Schuur, Jose Luis Vicente Villardon, Matthijs J. Warrens, Amaya Zarraga, Heungsun Hwang, Andrew Gelman, J.

ISBN 10: 1584886285 / ISBN 13: 9781584886280

Published by Chapman and Hall/CRC 2006-06-23, 2006

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Bibliographic Details

• Title: Multiple Correspondence Analysis and Related Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
• Publisher: Chapman and Hall/CRC 2006-06-23
• Publication Date: 2006
• Language: English
• ISBN 10: 1584886285
• ISBN 13: 9781584886280
• Binding: Hardcover
• Condition: New

About this title

Synopsis

As a generalization of simple correspondence analysis, multiple correspondence analysis (MCA) is a powerful technique for handling larger, more complex datasets, including the high-dimensional categorical data often encountered in the social sciences, marketing, health economics, and biomedical research. Until now, however, the literature on the subject has been scattered, leaving many in these fields no comprehensive resource from which to learn its theory, applications, and implementation. Multiple Correspondence Analysis and Related Methods gives a state-of-the-art description of this new field in an accessible, self-contained, textbook format. Explaining the methodology step-by-step, it offers an exhaustive survey of the different approaches taken by researchers from different statistical "schools" and explores a wide variety of application areas. Each chapter includes empirical examples that provide a practical understanding of the method and its interpretation, and most chapters end with a "Software Note" that discusses software and computational aspects. An appendix at the end of the book gives further computing details along with code written in the R language for performing MCA and related techniques. The code and the datasets used in the book are available for download from a supporting Web page. Providing a unique, multidisciplinary perspective, experts in MCA from both statistics and the social sciences contributed chapters to the book. The editors unified the notation and coordinated and cross-referenced the theory across all of the chapters, making the book read seamlessly. Practical, accessible, and thorough, Multiple Correspondence Analysis and Related Methods brings the theory and applications of MCA under one cover and provides a valuable addition to your statistical toolbox.

About the Author

Michael Greenacre, Jorg Blasius