Eric J. Beh - An Introduction to Correspondence Analysis

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7

1. Chapter 2
2. Chapter 3
3. Chapter 4
4. Chapter 5
5. Chapter 6
6. Chapter 7

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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 519.5/37dc23

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 (19322019)

and

John Clifford Gower (19302019)

May your legacy live on

In the late 2000s 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.