Download Multiple Correspondence Analysis And Related Methods full books in PDF, epub, and Kindle. Read online Multiple Correspondence Analysis And Related Methods ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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
Book Synopsis Multiple Correspondence Analysis and Related Methods by : Michael Greenacre
Download or read book Multiple Correspondence Analysis and Related Methods written by Michael Greenacre and published by CRC Press. This book was released on 2006-06-23 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
"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.
Book Synopsis Multiple Correspondence Analysis by : Brigitte Le Roux
Download or read book Multiple Correspondence Analysis written by Brigitte Le Roux and published by SAGE. This book was released on 2010 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
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
Book Synopsis Visualization and Verbalization of Data by : Jorg Blasius
Download or read book Visualization and Verbalization of Data written by Jorg Blasius and published by CRC Press. This book was released on 2014-04-10 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
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.
Book Synopsis Multiple Correspondence Analysis for the Social Sciences by : Johs. Hjellbrekke
Download or read book Multiple Correspondence Analysis for the Social Sciences written by Johs. Hjellbrekke and published by Routledge. This book was released on 2018-06-18 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Biplots in Practice by : Michael J. Greenacre
Download or read book Biplots in Practice written by Michael J. Greenacre and published by Fundacion BBVA. This book was released on 2010 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Correspondence Analysis in Practice, Third Edition by : Michael Greenacre
Download or read book Correspondence Analysis in Practice, Third Edition written by Michael Greenacre and published by CRC Press. This book was released on 2017-01-20 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Applied Correspondence Analysis by : Sten-Erik Clausen
Download or read book Applied Correspondence Analysis written by Sten-Erik Clausen and published by SAGE. This book was released on 1998-06 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA
Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA and published by STHDA. This book was released on 2017-08-23 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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
Book Synopsis Exploratory Multivariate Analysis by Example Using R by : Francois Husson
Download or read book Exploratory Multivariate Analysis by Example Using R written by Francois Husson and published by CRC Press. This book was released on 2017-04-25 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Presents a set of closely related techniques that facilitate the exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling, correspondence analysis, principal components analysis, and multiple dimensional preference scaling are explored in detail for strengths and weaknesses over a wide range of data types and research situations. "The introduction illustrates the methods with a small dataset. This approach is effective--in a few minutes, with no mathematical requirement, the reader can understand the capabilities, similarities, and differences of the methods. . . . Numerical examples facilitate learning. The authors use several examples with small datasets that illustrate very well the links and the differences between the methods. . . . we find this text very good and recommend it for graduate students and social science researchers, especially those who are interested in applying some of these methods and in knowing the relationship among them." --Journal of Marketing Research "Illustrate[s] the service Sage provides by making high-quality works on research methods available at modest prices. . . . The authors use several interesting examples of practical applications on data sets, ranging from contraception preferences, to pottery shards from archeological digs, to durable consumer goods from market research. These examples indicate the broad range of possible applications of the method to social science data." --Contemporary Sociology "The book is a bargain; it is clearly written." --Journal of Classification
Book Synopsis Metric Scaling by : Susan C. Weller
Download or read book Metric Scaling written by Susan C. Weller and published by SAGE. This book was released on 1990 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a set of closely related techniques that facilitate the exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling, correspondence analysis, principal components analysis, and multiple dimensional preference scaling are explored in detail for strengths and weaknesses over a wide range of data types and research situations. "The introduction illustrates the methods with a small dataset. This approach is effective--in a few minutes, with no mathematical requirement, the reader can understand the capabilities, similarities, and differences of the methods. . . . Numerical examples facilitate learning. The authors use several examples with small datasets that illustrate very well the links and the differences between the methods. . . . we find this text very good and recommend it for graduate students and social science researchers, especially those who are interested in applying some of these methods and in knowing the relationship among them." --Journal of Marketing Research "Illustrate[s] the service Sage provides by making high-quality works on research methods available at modest prices. . . . The authors use several interesting examples of practical applications on data sets, ranging from contraception preferences, to pottery shards from archeological digs, to durable consumer goods from market research. These examples indicate the broad range of possible applications of the method to social science data." --Contemporary Sociology "The book is a bargain; it is clearly written." --Journal of Classification
