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Multiple Correspondence Analysis

Multiple Correspondence Analysis
Author: Brigitte Le Roux
Publisher: SAGE
Total Pages: 129
Release: 2010
Genre: Mathematics
ISBN: 1412968976

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"Requiring no prior knowledge of correspondence analysis, this text provides anontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte Le Roux and Henry Rouanet, present the material in a practical manner, keeping the needs of researchers foremost in mind." "This supplementary text isappropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as forindividual researchers." --Book Jacket.


An Introduction to Correspondence Analysis

An Introduction to Correspondence Analysis
Author: Eric J. Beh
Publisher: John Wiley & Sons
Total Pages: 78
Release: 2021-03-29
Genre: Mathematics
ISBN: 1119041945

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Master the fundamentals of correspondence analysis with this illuminating resource An Introduction to Correspondence Analysis assists researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The accomplished academics and authors deliver a comprehensive and insightful treatment of the fundamentals of correspondence analysis, including the statistical and visual aspects of the subject. Written in three parts, the book begins by offering readers a description of two variants of correspondence analysis that can be applied to two-way contingency tables for nominal categories of variables. Part Two shifts the discussion to categories of ordinal variables and demonstrates how the ordered structure of these variables can be incorporated into a correspondence analysis. Part Three describes the analysis of multiple nominal categorical variables, including both multiple correspondence analysis and multi-way correspondence analysis. Readers will benefit from explanations of a wide variety of specific topics, for example: Simple correspondence analysis, including how to reduce multidimensional space, measuring symmetric associations with the Pearson Ratio, constructing low-dimensional displays, and detecting statistically significant points Non-symmetrical correspondence analysis, including quantifying asymmetric associations Simple ordinal correspondence analysis, including how to decompose the Pearson Residual for ordinal variables Multiple correspondence analysis, including crisp coding and the indicator matrix, the Burt Matrix, and stacking Multi-way correspondence analysis, including symmetric multi-way analysis Perfect for researchers who seek to improve their understanding of key concepts in the graphical analysis of categorical data, An Introduction to Correspondence Analysis will also assist readers already familiar with correspondence analysis who wish to review the theoretical and foundational underpinnings of crucial concepts.


Multiple Correspondence Analysis for the Social Sciences

Multiple Correspondence Analysis for the Social Sciences
Author: Johs. Hjellbrekke
Publisher: Routledge
Total Pages: 118
Release: 2018-06-18
Genre: Social Science
ISBN: 1315516241

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Multiple correspondence analysis (MCA) is a statistical technique that first and foremost has become known through the work of the late Pierre Bourdieu (1930–2002). This book will introduce readers to the fundamental properties, procedures and rules of interpretation of the most commonly used forms of correspondence analysis. The book is written as a non-technical introduction, intended for the advanced undergraduate level and onwards. MCA represents and models data sets as clouds of points in a multidimensional Euclidean space. The interpretation of the data is based on these clouds of points. In seven chapters, this non-technical book will provide the reader with a comprehensive introduction and the needed knowledge to do analyses on his/her own: CA, MCA, specific MCA, the integration of MCA and variance analysis, of MCA and ascending hierarchical cluster analysis and class-specific MCA on subgroups. Special attention will be given to the construction of social spaces, to the construction of typologies and to group internal oppositions. This is a book on data analysis for the social sciences rather than a book on statistics. The main emphasis is on how to apply MCA to the analysis of practical research questions. It does not require a solid understanding of statistics and/or mathematics, and provides the reader with the needed knowledge to do analyses on his/her own.


Correspondence Analysis and Data Coding with Java and R

Correspondence Analysis and Data Coding with Java and R
Author: Fionn Murtagh
Publisher: CRC Press
Total Pages: 253
Release: 2005-05-26
Genre: Mathematics
ISBN: 1420034944

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Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of application emerging, its significance is greater


Correspondence Analysis Handbook

Correspondence Analysis Handbook
Author: Benzecri
Publisher: CRC Press
Total Pages: 684
Release: 1992-01-22
Genre: Mathematics
ISBN: 058536303X

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This practical reference/text presents a complete introduction to the practice of data analysis - clarifying the geometrical language used, explaining the formulae, reviewing linear algebra and multidimensional Euclidean geometry, and including proofs of results. It is intended as either a self-study guide for professionals involved in experimental


Theory and Applications of Correspondence Analysis

Theory and Applications of Correspondence Analysis
Author: Michael J. Greenacre
Publisher:
Total Pages: 386
Release: 1984
Genre: Mathematics
ISBN:

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Geometric concepts in multidimensional space; Simple illustrations of correspondence analysis; Theory of correspondence analysis and equivalent approaches; Multiple correspondence analysis; Correspondence analysis of ratings and preferences; Use of correspondence analysis in discriminant analysis, classification, regression and cluster analysis; Special topics; Applications of correspondence analysis.


Applied Correspondence Analysis

Applied Correspondence Analysis
Author: Sten-Erik Clausen
Publisher: SAGE
Total Pages: 230
Release: 1998-06
Genre: Social Science
ISBN: 9780761911159

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This volume provides readers with a simple, non-technical introduction to correspondence analysis (CA), a technique for summarily describing the relationships among categorical variables in large tables. It begins with the history and logic of CA. The author shows readers the steps to the analysis: category profiles and masses are computed, the distances between these points calculated and the best-fitting space of n-dimensions located. There are glossaries on appropriate programs from SAS and SPSS for doing CA and the book concludes with a comparison of CA and log-linear models.


Multiple Correspondence Analysis and Related Methods

Multiple Correspondence Analysis and Related Methods
Author: Michael Greenacre
Publisher: CRC Press
Total Pages: 607
Release: 2006-06-23
Genre: Mathematics
ISBN: 1420011316

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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 su


Correspondence Analysis in the Social Sciences

Correspondence Analysis in the Social Sciences
Author: Michael Greenacre
Publisher: Academic Press
Total Pages: 400
Release: 1994-09-21
Genre: Business & Economics
ISBN:

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The first part of the book deals with basic concepts of correspondence analysis and related methods for analyzing cross-tabulations. It then looks at the multivariate case when there are several variables of interest, including the relationship to cluster analysis, factor analysis and reliability of measurement. Applications to longitudinal data: event history data, panel data and trend data are demonstrated.