Multiple Correspondence Analysis and Related Methods

<p>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.<br><br>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.<br><br>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.</p>