Read more
Devises a rigorous, intuitive methodology for case-study research, helping social scientists and analysts make better inferences from qualitative evidence. Bayesianism provides guidance for rational reasoning under uncertainty, to make well-justified assessments about how strongly the information in hand supports one explanation over rivals.
List of contents
Contents; Acknowledgements; Part I. Foundations: 1. Introduction: Bayesian reasoning for qualitative research; 2. Fundamentals of Bayesian probability; Part II. Operationalizing Bayesian Reasoning in Qualitative Research: 3. Heuristic Bayesian reasoning; 4. Explicit Bayesian analysis; 5. Bayesian analysis with multiple cases; 6. Hypotheses and priors revisited; 7. Scrutinizing qualitative research; Part III. Bayesianism in Methodological Perspective: 8. Comparing logical Bayesianism to frequentism; 9. A unified framework for inference; Part IV. Bayesian Implications for Research Design: 10. Iterative research; 11. Test strength; 12. Case selection; 13. Worked examples; References; Contents; Index.
About the author
Tasha Fairfield is an Associate Professor at the London School of Economics, with a Ph.D in political science from the University of California, Berkeley, and an M.S. in physics from Stanford University. Her publications include Private Wealth and Public Revenue in Latin America (Cambridge, 2015), which won the Donna Lee Van Cott Book Award.Andrew E. Charman is a Lecturer and Researcher in Physics at the University of California, Berkeley, and an expert in Bayesian statistics. Beyond analyzing measurements of antimatter and the foundations of quantum mechanics, he has explored methods for optimal congressional apportionment and statistical mechanical models of gerrymandering. His previous work with Tasha Fairfield received APSA's QMMR Sage Paper Award.
Summary
Devises a rigorous, intuitive methodology for case-study research, helping social scientists and analysts make better inferences from qualitative evidence. Bayesianism provides guidance for rational reasoning under uncertainty, to make well-justified assessments about how strongly the information in hand supports one explanation over rivals.
Foreword
Provides guidance for Bayesian updating in case study, process-tracing, and comparative research, in order to refine intuition and improve inferences from qualitative evidence.
Additional text
In this book, Fairfield and Charman develop the most systematic and concrete approach to qualitative research in political science. The Bayesian framework provides a clear structure for learning from evidence within and across cases, and the book provides clear and actionable guidelines that scholars can take on board. Readers will learn a great deal from this book about designing and carrying out rigorous and transparent qualitative research. Hillel David Soifer, Associate Professor, Department of Political Science, Temple University
