Multiple Correspondence Analysis for the Social Sciences
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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....
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.
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.
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