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A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. This book explores analytic induction, an approach to the analysis of cross-case evidence on qualitative outcomes that has deep roots in sociology. A popular research technique in the early decades of empirical sociology, analytic induction differs fundamentally as a method of social research from conventional variation-based approaches. In Analytic Induction for Social Research, Charles C. Ragin demonstrates that much is gained from systematizing analytic induction. The approach he introduces here offers a new template for conducting cross-case analysis and provides a new set of tools for answering common research questions that existing methods cannot address

Charles C. Ragin's The Comparative Method proposes a synthetic strategy, based on an application of Boolean algebra, that combines the strengths of both qualitative and quantitative sociology. Elegantly accessible and germane to the work of all the social sciences, and now updated with a new introduction, this book will continue to garner interest, debate, and praise

For over twenty years Charles C. Ragin has been at the forefront of the development of innovative methods for social scientists. In Redesigning Social Inquiry, he continues his campaign to revitalize the field, challenging major aspects of the conventional template for social science research while offering a clear alternative. Redesigning Social Inquiry provides a substantive critique of the standard approach to social research--namely, assessing the relative importance of causal variables drawn from competing theories. Instead, Ragin proposes the use of set-theoretic methods to find a middle.

Because of its inherently asymmetric nature, set-theoretic analysis offers many interesting contrasts with analysis based on correlations. Until recently, however, social scientists have been slow to embrace set-theoretic approaches. The perception was that this type of analysis is restricted to primitive, binary variables and that it has little or no tolerance for error. With the advent of "fuzzy" sets and the recognition that even rough set-theoretic relations are relevant to theory, these old barriers have crumbled. This paper advances the set-theoretic approach by presenting simple descriptive measures that can be used to evaluate set-theoretic relationships, especially relations between fuzzy sets. The first measure, "consistency," assesses the degree to which a subset relation has been approximated, whereas the second measure, "coverage," assesses the empirical relevance of a consistent subset. This paper demonstrates further that set-theoretic coverage can be partitioned in a manner somewhat analogous to the partitioning of explained variation in multiple regression analysis.