Multiple correspondence analysis and related methods
著者
書誌事項
Multiple correspondence analysis and related methods
（Statistics in the social and behavioral sciences series）
Chapman & Hall/CRC, c2006
- : hardcover
大学図書館所蔵 件 / 全19件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. [553]-574) and index
内容説明・目次
内容説明
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.
目次
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 x 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.
「Nielsen BookData」 より
