Correspondence analysis in practice.
2nd ed.

*(English)*Zbl 1198.62061
Interdisciplinary Statistics. Boca Raton, FL: Chapman & Hall/CRC (ISBN 978-1-58488-616-7/hbk; 978-1-4200-1123-4/ebook). xiii, 280 p. (2007).

This book is the totally revised and extended edition of the original text, first published in 1993.

Publisher’s description: Drawing on the author’s experience in social and environmental research, this second edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries.

New in this edition: Five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis; Substantially more figures and tables than in the first edition; A computational appendix that provides the R commands that correspond to most of the analyses featured throughout the book, making it easy for readers to reproduce the analyses.

With 33 years of CA experience, the author demonstrates how to use uncomplicated, relatively non-mathematical techniques to translate complex tabular data into more readable graphical forms. CA and its variants, multiple CA (MCA) and joint CA (JCA), are suitable for analyses in various fields, including marketing research, social and environmental sciences, biochemistry, and more.

Publisher’s description: Drawing on the author’s experience in social and environmental research, this second edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries.

New in this edition: Five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis; Substantially more figures and tables than in the first edition; A computational appendix that provides the R commands that correspond to most of the analyses featured throughout the book, making it easy for readers to reproduce the analyses.

With 33 years of CA experience, the author demonstrates how to use uncomplicated, relatively non-mathematical techniques to translate complex tabular data into more readable graphical forms. CA and its variants, multiple CA (MCA) and joint CA (JCA), are suitable for analyses in various fields, including marketing research, social and environmental sciences, biochemistry, and more.