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Multiple Correspondence Analysis
Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind. Key Features Readers learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations. They also learn how to perform structured data analysis and to draw inferential conclusions from MCA. The text uses real examples to help explain concepts. The authors stress the distinctive capacity of MCA to handle full-scale research studies. This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.
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.
Combinatorial Inference in Geometric Data Analysis
Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud of points. Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial framework. It focuses particularly on the comparison of a group of observations to a reference population (combinatorial test) or to a reference value of a location parameter (geometric test), and on problems of homogenei...
Empirical Investigations of Social Space
This book provides an in-depth view on Bourdieu’s empirical work, thereby specially focusing on the construction of the social space and including the concept of the habitus. Themes described in the book include amongst others: • the theory and methodology for the construction of “social spaces”, • the relation between various “fields” and “the field of power”, • formal construction and empirical observation of habitus, • the formation, accumulation, differentiation of and conversion between different forms of capital, • relations in geometric data analysis. The book also includes contributions regarding particular applications of Bourdieu’s methodology to traditional and new areas of research, such as the analysis of institutional, international and transnational fields. It further provides a systematic introduction into the empirical construction of the social space.
Quantifying Theory: Pierre Bourdieu
Pierre Bourdieu’s contributions to the theory and practice of social research are far reaching. Possibly the most prominent sociologist in recent times, his work has touched on a myriad of topics and has influenced scholars in multiple disciplines. Throughout Bourdieu’s work, emphasis is placed on the linkage between the practice of social research and its relationship to social theory. This book honours Bourdieu’s commitment to the inextricable relationship between social theory and research in social science. In this volume, authors from all over the world utilize key concepts coined by Bourdieu, specifically his concept of capitals, habitus, and the field, and attempt to test them u...
Trust
Trust, as Simmel noted, is a hypothesis regarding future behavior that is certain enough to serve as a basis for practical conduct. To trust another person (or collectivity or institution) is intermediate between knowledge and ignorance. Simmel was one of many social scientists (e.g., Tonnies, Durkheim, Parsons) who have contended that trust is one of the most important integrative forces within society. Modernization and its attendant social isolation, in the face of massive global changes, underscore the need to reexamine trust in all its multivariate and multidisciplinary character. This anthology presents twelve studies of trust. Some are conceptual, theoretical analyses, while others use historical data on societies, national surveys or cross-national comparative studies to test hypotheses.
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.
Regression Inside Out
Demonstrates new ways to extract knowledge from statistical data and unlock more nuanced interpretations than has previously been possible.
Statistical Learning and Data Science
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor
Fashionable Masculinities
Fashionable Masculinities explores the expression of masculinities through constructions of fashion, identity, style and appearance as the third decade of the new millennium begins: a contradictory and precarious moment when masculinities are defined by protests and pandemics whilst being problematized across class, ethnicity, race, gender and sexuality. Whilst a majority of men might still define themselves as ‘traditional,’ post-millennials are now talking about how they envision a future without gender boundaries and borders. Rather than being defined as a gender, masculinity has now become a style that can be worn and performed as traditional and normative codes of masculinity are modulated and manipulated. This volume includes original essays on musical pop sensation Harry Styles, rapper and producer “Puff Daddy” Sean Combs, lumbersexuals, spornosexuals, sexy daddies, and aging cool black daddies. Bringing together contributions from leading scholars, this book interrogates and challenges the meaning of masculinities and the ways that they are experienced and lived.
