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Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework.
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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.
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Social Inquiry and Bayesian Inference: Rethinking Qualitative Research (Strategies for Social Inquiry)
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Hardcover. Condición: new. Hardcover. Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781108421645
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Social Inquiry and Bayesian Inference: Rethinking Qualitative Research
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Hardcover. Condición: new. Hardcover. Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781108421645
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Social Inquiry and Bayesian Inference: Rethinking Qualitative Research (Strategies for Social Inquiry)
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 assess. Nº de ref. del artículo: 509943349
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Hardcover. Condición: new. Hardcover. Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781108421645
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