Multiple Correspondence Analysis and Related Methods
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?2 Distances
advanced categorical data modeling
Burt Matrix
canonical
Canonical Correlation Analysis
categorical data analysis
Categorical PCA
Categorical Variables
Category=JHBC
component
contingency
Contingency Tables
correlation
Correspondence Analysis
data
Data Matrix
Data Set
Diagonal Block
discriminant
Dual Scaling
Dummy Variables
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Generalize PCA
Generalized Canonical Analysis
health economics analytics
indicator
Indicator Matrix
matrix
MCA Solution
MFA
Multiple Correspondence Analysis
multivariate statistical techniques
principal
Principal Coordinates
Principal Inertias
R programming for statistics
Row Margin
Simple CA
social science statistics
survey data interpretation
table
Total Inertia
Vice Versa
Weighted PCA
Χ2 Distances
Product details
- ISBN 9781584886280
- Weight: 952g
- Dimensions: 156 x 234mm
- Publication Date: 23 Jun 2006
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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 the theory and applications of MCA under one cover and provides a valuable addition to your statistical toolbox.
Michael Greenacre, Jorg Blasius
