An Introduction to Correspondence Analysis. Eric J. Beh
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An Introduction to Correspondence Analysis
Eric J. Beh
School of Mathematical & Physical Sciences,
University of Newcastle, Australia
Rosaria Lombardo
Department of Economics,
University of Campania “Luigi Vanvitelli,” Italy
This edition first published 2021
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Library of Congress Cataloging-in-Publication Data
Names: Beh, Eric J., author. | Lombardo, Rosaria, author.
Title: An introduction to correspondence analysis / Eric J. Beh, Rosaria Lombardo.
Description: Hoboken, NJ : Wiley, 2021. | Includes bibliographical references and index.
Identifiers: LCCN 2020034475 (print) | LCCN 2020034476 (ebook) | ISBN 9781119041948 (cloth) | ISBN 9781119041962 (adobe pdf) | ISBN 9781119041979 (epub)
Subjects: LCSH: Correspondence analysis (Statistics)
Classification: LCC QA278.5 .B43 2021 (print) | LCC QA278.5 (ebook) | DDC 519.5/37–dc23
LC record available at https://lccn.loc.gov/2020034475
LC ebook record available at https://lccn.loc.gov/2020034476
Cover Design: Wiley
Cover Image: © Giovanna Lombardo, p.zza Giovanni XXIII, Castellammare di Stabia (NA) Italy
To Rosey and Alex …
To Donato, Renato and Andrea …
… for your patience, support and always being there
… Eric J. Beh & Rosaria Lombardo
In memory of two pioneers
Jean-Paul Benzecri (1932–2019)
and
John Clifford Gower (1930–2019)
May your legacy live on
Preface
In the late 2000’s we embarked on a rather ambitious project to write a book that covered an extensive array of topics on correspondence analysis. This work resulted in the publication in 2014 of Correspondence Analysis: Theory, Practice and New Strategies. The attempt in that book was to provide a comprehensive technical, computational, theoretical and practical description of a variety of correspondence analysis techniques. These focused largely on the analysis of nominal and ordinal categorical variables with a symmetric and asymmetric association structure. We not only described these techniques for two variables but also discussed how they can be used and adapted for analysing multiple categorical variable.
Irrespective of the benefits and faults of that book, we attempted to give an extensive number of different perspectives. While our general flavour may be more in line with the French approach to correspondence analysis we also tried to approach our discussion by incorporating the British/American conventions of categorical data analysis commonly seen throughout the world. A priority we had was to not just provide a synthesis of a broad amount of the correspondence analysis literature from all around the world but to also discuss the role that the origin of categorical data analysis had on the development of correspondence analysis.
From writing the 2014 book we quickly realised that it may contain too much information for someone who was not well versed in some of the more subtle or obscure aspects of correspondence analysis. We also became aware that some didn’t feel the need to wade through an extensive literature review and technical discussion, but instead wished to focus on the key features of the analysis. So, after taking some time to take a deep breath and stretch our collective muscles, we dived back into writing again to focus on a book with more of an introductory, or tutorial, flavour than the first book allowed. This book is the result of those deep breaths and muscle stretches.
There are many contributions in the statistics, and allied, literature that provide an introduction to correspondence analysis. However many of these