Multi-Method Social Science
Combining Qualitative and Quantitative Tools
Published on 19. October 2016
978-1-316-79261-2 (ISBN)
Description
Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
File size
2688,71 MB
ISBN-13
978-1-316-79261-2 (9781316792612)
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Schweitzer Classification
Other editions
Additional editions
Book
09/2016
Cambridge University Press
€49.80
Shipment within 15-20 days
Book
09/2016
Cambridge University Press
€108.50
Shipment within 15-20 days
Person
Jason Seawright is an Associate Professor in the Department of Political Science at Northwestern University. He has written extensively about multi-method research and has published in journals including Political Analysis, Political Research Quarterly, Sociological Methodology, and Sociological Methods and Research. He was the author or co-author of several chapters in Rethinking Social Inquiry: Diverse Tools, Shared Standards, edited by Henry E. Brady and David Collier. He has taught courses on multi-method research design at institutes worldwide.
Content
1. Integrative multi-method research; 2. Causation as a shared standard; 3. Using case studies to test and define regressions; 4. Case selection after regression; 5. Combining case studies and matching; 6. Combining case studies and natural experiments; 7. Embedding case studies within experiments; 8. Multi-method case studies; Appendix A. Qualitative causal models and the potential outcomes framework; Bibliography.
