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Bourdieu and Data Analysis

Methodological Principles and Practice

Michael Grenfell and Frédéric Lebaron

Uniquely amongst the numerous publications to appear on the work of the French social theorist Pierre Bourdieu, this book deals with data analysis, examining a range of techniques and instruments. After an introductory chapter outlining the key principles of Bourdieu’s theory, the book presents detailed examples of data being collected and analysed in a Bourdieusian way across various social science contexts. Both qualitative and quantitative methods are addressed, including analysis of the strengths and weaknesses of each method, as are common data collection procedures such as interview, observation and questionnaire. Examples of Multiple Correspondence Analysis are an important feature of the book, since this was an approach particularly favoured by Bourdieu. In each case study, the pros and cons of different approaches are highlighted and the qualitative/quantitative debate is thoroughly explored. Overall, the book offers readers a blueprint to develop their own methodological plans for using Bourdieu in research practice.
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Notes on Contributors


RAMÓN ÁLVAREZ ESTEBAN is Associate Professor at Universidad de León. His areas of specialism are textual data, statistical analysis of socioeconomic variables, multivariate data analysis in marketing research, stability and resampling methods in multivariate methods. His key publications include ‘Rating of products through scores and free-text assertions: Comparing and combining both’, Food Quality and Preference, 19, 122–34 (2008); ‘Statistical Study of Judicial Practices’, Lecture Notes in Artificial Intelligence LNCS 3369, 25–35 (2005) (with M. Ayuso and M. Bécue-Bertaut); and ‘Assessing the stability of supplementary elements on principal axes maps through bootstrap resampling. Contribution to interpretation in textual analysis’ (with O. Valencia and M. Bécue-Bertaut) in C. H. Skiadas (ed.), Statistics for Industry and Technology, Advances in Data Analysis.


MÓNICA BÉCUE-BERTAUT is Associate Professor at Universitat Politècnica de Catalunya. Her areas of specialism are multidimensional analysis and applications to survey data, analysis of mixed textual and contextual data, argumentative structure in law domain and sensometrics. Her key publications include ‘Rhetorical strategy in forensic closing speeches: Multidimensional statistics-based methodology’ in Journal of Classification (in press) (with B. Kostov, A. Morin and G. Naro); ‘Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data’ in Computational Statistics & Data Analysis, 52, 3255–68 (with J. Pagès) (2008); and ‘A principal axes method for comparing contingency tables: MFACT’ in Computational Statistics & Data Analysis, 52, 481–503 (with J. Pagès) (2004).

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