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Multiple Correspondence Analysis and Related Methods
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 Handbook
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
Correspondence Analysis in Practice
Drawing on the author’s experience in social and environmental research, Correspondence Analysis in Practice, Second Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. New to the Second Edition • Five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis • Substantially more figures and tabl...
Correspondence Analysis and Data Coding with Java and R
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
Geometric Data Analysis
Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.
The Oxford Handbook of Pierre Bourdieu
The Oxford Handbook of Pierre Bourdieu examines the legacy of one of the most influential social thinkers of the last half-century. Taken together, these writings offer a comprehensive overview of Bourdieu's biography, his main theoretical ideas, and his ongoing influence on the social sciences.
Component and Correspondence Analysis
Contributions to this work begin from a homogeneous-analytic point of view, then go further to analyze continuous variables, extending the geometrical approach of Gifi, and applying functional analytic techniques to problems involving replicated time series data (which are not subject to classical correspondence analysis and principal component analysis). Closing chapters address probability coding (which is related to fuzzy coding), and two approaches to component analysis: optimal scaling (which uses montone splines) embedded in a more classical statistical framework; and the connection between explorative multivariate data analysis and confirmation analysis based on statistical modelling.
Applied Correspondence Analysis
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.
Visualization and Verbalization of Data
Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications. The first part of the book explains the historical origins of correspondence analysis and associated methods. The second part concentrates on the contributions made by the school of Jean-Paul Benzécri and related movements, such as social space and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an international audience. Throughout the text, well-known experts illustrate the use of the methods in practice. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, different possibilities of recoding data prior to visualization, and the application of duality diagram theory to the analysis of a contingency table.
Multiple Correspondence Analysis
"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.
