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Multiple Correspondence Analysis and Related Methods (Hardcover)
Loot Price: R4,477
Discovery Miles 44 770
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Multiple Correspondence Analysis and Related Methods (Hardcover)
Series: Statistics in the Social and Behavioral Sciences Series
Expected to ship within 12 - 17 working days
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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. 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. 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 thetheory and applications of MCA under one
cover and provides a valuable addition to your statistical toolbox.
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