Chapman and Hall/CRC
310 pages | 113 B/W Illus.
Drawing on the author’s 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more.
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
"Perfect time for this beautifully (R+LaTeX) implemented book! It has now 30 exactly-eight-page chapters (5 brand new!), 167 wonderful "exhibits" (typically 5 or 6 per chapter), informative marginal notes, useful chapter summaries, and wisely isolated appendices of 64 extra pages including well documented R scripts (available on website), a compact walk through the SVD-centered matrix theory, and a carefully annotated bibliography. This book truly contains and conveys a huge amount of practical knowledge related to Correspondence Analysis. Really appreciate the new edition!
Based on the fact that the previous edition was an instant success in my circles, I am willing to predict that the new edition will cha(lle)nge the mindsets in the rapidly growing areas of Computational Social Sciences and Digital Humanities as well as in numerous data-driven branches of Natural Sciences. This is important, because our era of data - larger than ever and complex like chaos - requires several skills from statisticians and other data scientists. We must see behind the patterns of numbers hidden in matrices and arrays. We are not afraid of coding, recoding, programming, or modelling. We want to visualize, analyze, interpret, understand, and communicate. These are the core themes of Correspondence Analysis. And this book is THE book for learning these skills."
—Kimmo Vehkalahti, Fellow of the Teachers' Academy, University of Helsinki
"This great book has become greater, and not just by the extra five chapters. Michael Greenacre has extended the scope of correspondence analysis in this third edition into new and interesting areas, such as network analysis and compositional data. Like the previous edition, this one sticks to the module format, making it ideal as a teaching text. This is an authoritative book by one of the world experts, written in an accessible style."
—Trevor Hastie, Statistics Department, Stanford University
"Michael Greenacre’s book is an innovative and illuminating book which covers various fields of correspondence analysis to different areas of research. It is a must for practitioners and statisticians working in the area of correspondence analysis and other visualization methods."
—Jörg Blasius, Professor of Sociology, University of Bonn, Germany
"This book is wonderfully compact, delivering just the right level of detail to give the reader a robust understanding of the subject: not just how to do it, but why it works, which is so crucial when confronting unusual analytical problems. Each chapter is exactly eight pages long, which is one of my favourite features in any statistics textbook. The reader knows that each topic can be digested in a manageable period of time. This format has been retained since the second edition, which I first encountered when I was called on to carry out a CA and had to teach myself it over a weekend. I enjoyed having Greenacre as my guide then, and I expect that new readers will feel the same now. He has the knack of making new methods approachable and intuitive without sacrificing important detail."
—Robert Grant, Kingston University and St George’s, University of London
"The third instalment of Correspondence Analysis in Practice continues to deliver an excellent guide on the application of correspondence analysis but with a twist. This time, the third edition includes far more discussion on data structures not seen before in previous, or more recent, books on correspondence analysis. Thus the book will continue to reach out to those researchers and students who want to learn more about the ways of visualising categorical data. Michael has continued with his unique 8-pages per chapter format ensuring that this edition remains as engaging and very easy to read and follow as ever."
—Eric Beh, University of Newcastle
Praise for the Second Edition:
"…this book is pleasant to read, accessible, and easy to digest. … the book provides the reader with all he or she needs for a proper use of CA and its main extensions. There is enough theory for a valid interpretation of the analysis, in particular what can and what cannot be read on a map. Learning is easy thanks to the pleasant style of the author, the choice of the various illustrative examples, and the appropriate format of the presentation. The book can be read at different levels depending on the reader’s background in mathematics. Therefore, it will be useful to a large number of users, researchers and professionals in all branches, especially in human sciences, ecology, linguistic, etc. It will also be of great interest for students in statistics and, of course, for teachers. I highly recommend this volume to a very wide readership."
—Computational Statistics & Data Analysis, Vol. 53, 2009
“…a brilliant book written by an experienced writer…this kind of insight is something that practically every book could have. I would truly recommend this book for everyone who is interested in analyzing and visualizing categorical data…will surely find lots of use for this book.”
—Kimmo Vehkalahti, University of Helsinki, Finland, International Statistical Review, 2008
"This is a nice book for all those who wish to acquaint themselves with the versatile methodology of correspondence analysis and the way it can be used for the analysis and visualization of data arriving typically from fields of social, environmental, and health sciences, marketing, and economics. Numerous examples provide a real flavour of the possibilities of the method."
"This writer, a well-experienced researcher, makes complicated things seem easy and easily understood . . . I really enjoyed reading this book . . . I would recommend this book for everyone who works with categorical data and at least one time faced the problem of visualizing such data, especially those interested in correspondence analysis techniques will surely use this book as their standard reference."
– Dimitris Karlis, Athens University of Economics, Psychometrika, March 2009, Vol. 74, No. 1
"I really enjoyed reading this book. This is in spite of the fact that I must confess to not belonging to the crowd of correspondence analysis (CA) fans. Quite on the contrary, my previous encounters with CA were mostly negative – due to elaborate terminology and hard-to-understand graphical presentations, seemingly without much underlying foundation. My view changed profoundly after reading this text! … The text allows for effective learning from negative examples (e.g. for symmetric and asymmetric maps), but mostly it draws on very nice and carefully chosen data examples whose analysis is presented in a beautiful way. If you are tired of your own work, I recommend to page through these examples to be assured that good statistics is always enjoyable. In this spirit, this book could be on a suggested reading list both for students and for experienced practitioners."
- Marek Brabec in ISCB, June 2019
Scatterplots and Maps
Profiles and the Profile Space
Masses and Centroids
Chi-Square Distance and Inertia
Plotting Chi-Square Distances
Reduction of Dimensionality
Symmetry of Row and Column Analyses
Three More Examples
Contributions to Inertia
Correspondence Analysis Biplots
Transition and Regression Relationships
Clustering Rows and Columns
Multiple Correspondence Analysis
Joint Correspondence Analysis
Scaling Properties of MCA
Subset Correspondence Analysis
Analysis of Matches Matrices
Analysis of Square Tables
Correspondence Analysis of Networks
Canonical Correspondence Analysis
Co-Inertia and Co-Correspondence Analysis
Aspects of Stability and Inference
Appendix A: Theory of Correspondence Analysis
Appendix B: Computation of Correspondence Analysis
Appendix C: Bibliography of Correspondence Analysis
Appendix D: Glossary of Terms
Appendix E: Epilogue