Multiple Correspondence Analysis for the Social Sciences

Multiple Correspondence Analysis for the Social Sciences PDF

Author: Johs. Hjellbrekke

Publisher: Routledge

Published: 2018-06-18

Total Pages: 118

ISBN-13: 1315516241

DOWNLOAD EBOOK →

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.

Multiple Correspondence Analysis

Multiple Correspondence Analysis PDF

Author: Brigitte Le Roux

Publisher: SAGE

Published: 2010

Total Pages: 129

ISBN-13: 1412968976

DOWNLOAD EBOOK →

"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.

Multiple Correspondence Analysis and Related Methods

Multiple Correspondence Analysis and Related Methods PDF

Author: Michael Greenacre

Publisher: CRC Press

Published: 2006-06-23

Total Pages: 607

ISBN-13: 1420011316

DOWNLOAD EBOOK →

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

Practical Guide To Principal Component Methods in R

Practical Guide To Principal Component Methods in R PDF

Author: Alboukadel KASSAMBARA

Publisher: STHDA

Published: 2017-08-23

Total Pages: 169

ISBN-13: 1975721136

DOWNLOAD EBOOK →

Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

Visualization and Verbalization of Data

Visualization and Verbalization of Data PDF

Author: Jorg Blasius

Publisher: CRC Press

Published: 2014-04-10

Total Pages: 382

ISBN-13: 1466589817

DOWNLOAD EBOOK →

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

Correspondence Analysis in Practice, Third Edition

Correspondence Analysis in Practice, Third Edition PDF

Author: Michael Greenacre

Publisher: CRC Press

Published: 2017-01-20

Total Pages: 571

ISBN-13: 1315352958

DOWNLOAD EBOOK →

Drawing on the author’s 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.

Applied Correspondence Analysis

Applied Correspondence Analysis PDF

Author: Sten-Erik Clausen

Publisher: SAGE

Published: 1998-06

Total Pages: 230

ISBN-13: 9780761911159

DOWNLOAD EBOOK →

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.

Biplots in Practice

Biplots in Practice PDF

Author: Michael J. Greenacre

Publisher: Fundacion BBVA

Published: 2010

Total Pages: 241

ISBN-13: 8492384689

DOWNLOAD EBOOK →

Este libro explica las aplicaciones específicas y las interpretaciones del biplot en muchas áreas del análisis multivariante. regresión, modelos lineales generalizados, análisis de componentes principales, análisis de correspondencias y análisis discriminante.

Exploratory Multivariate Analysis by Example Using R

Exploratory Multivariate Analysis by Example Using R PDF

Author: Francois Husson

Publisher: CRC Press

Published: 2017-04-25

Total Pages: 263

ISBN-13: 1315301865

DOWNLOAD EBOOK →

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) a