Multiple Correspondence Analysis and Related Methods
Published on 23. June 2006
608 pages
978-1-4200-1131-9 (ISBN)
Description
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 su
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Researchers and graduate students from statistics, marketing, social sciences, health economics, and biomedical research.
Illustrations
133 s/w Abbildungen, 108 s/w Tabellen
133 b/w images, 108 tables and 301 equations
File size
8,90 MB
ISBN-13
978-1-4200-1131-9 (9781420011319)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael Greenacre | Jorg Blasius
Multiple Correspondence Analysis and Related Methods
Book
06/2006
1st Edition
Chapman & Hall/CRC
€234.20
Shipment within 15-20 days
Persons
Michael Greenacre, Jorg Blasius
Content
Correspondence Analysis and Related Methods in Practice. From Simple to Multiple Correspondence Analysis. Divided by a Common Language: Analyzing and Visualizing Two-Way Arrays. Nonlinear Principal Component Analysis and Related Techniques. The Geometric Analysis of Structured Individuals Variables Tables. Correlational Structure of Multi-Choice Data as Viewed from Dual Scaling. Validation Techniques in Multiple Correspondence Analysis. Multiple Correspondence Analysis of Subsets of Response Categories. Scaling Unidimensional Models with Multiple Correspondence Analysis. The Unfolding Fallacy Unveiled: A Comparison of Multiple Correspondence Analysis and Non-Metric IRT Models. Regularized Multiple Correspondence Analysis. The Evaluation of "Don't Know" Responses by Generalized Canonical Analysis. Multiple Factor Analysis for Contingency Tables. Simultaneous Analysis. Multiple Factor Analysis of Mixed Tables. Correspondence Analysis and Classification. Multiblock Canonical Correlation Analysis for Categorical Variables: Application to Epidemiological Data. Projection Pursuit Approach for Categorical Data. Correspondence Analysis and Categorical Conjoint Measurement. A Three-Step Approach to Assessing the Behavior of Survey Items in Cross-National Research using Biplots. Additive and Multiplicative Models for Three-Way Contingency Tables: Darroch (1974) Revisited. A New Model for Visualizing Interactions in Analysis of Variance. Logistic Biplots. Appendix: Computational of Multiple Correspondence Analysis, with Code in R.
