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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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