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Author: Tasha Fairfield
Publisher: Cambridge University Press
ISBN: 1108382029
Category : Political Science
Languages : en
Pages : 683
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Book Description
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.
Author: Tasha Fairfield
Publisher: Cambridge University Press
ISBN: 1108382029
Category : Political Science
Languages : en
Pages : 683
Get Book Here
Book Description
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.
Author: Tasha Fairfield
Publisher: Cambridge University Press
ISBN: 1108421644
Category : Political Science
Languages : en
Pages : 683
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Book Description
Provides guidance for Bayesian updating in case study, process-tracing, and comparative research, in order to refine intuition and improve inferences from qualitative evidence.
Author: Henry E. Brady
Publisher: Rowman & Littlefield
ISBN: 9780742511255
Category : Social Science
Languages : en
Pages : 388
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Book Description
'Rethinking Social Inquiry' offers a cohesive set of reflections on the quest for common standards drawn from quantitative methodology. The contributors argue that these standards must be drawn from exemplary qualitative research as well as the best quantitative studies.
Author: Ivan Jeliazkov
Publisher: John Wiley & Sons
ISBN: 1118771125
Category : Mathematics
Languages : en
Pages : 266
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Book Description
Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Author: Gary King
Publisher: Princeton University Press
ISBN: 0691034710
Category : Social Science
Languages : en
Pages : 259
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Book Description
Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?
Author: Andrew Bennett
Publisher: Cambridge University Press
ISBN: 1107044529
Category : Philosophy
Languages : en
Pages : 345
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Book Description
This book provides empirically grounded conceptual, design and practical advice on conducting process tracing, a key method of qualitative research.
Author: Gary King
Publisher: Cambridge University Press
ISBN: 9780521542807
Category : Nature
Languages : en
Pages : 436
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Book Description
Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
Author: Simon Jackman
Publisher: John Wiley & Sons
ISBN: 9780470686638
Category : Mathematics
Languages : en
Pages : 598
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Book Description
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
Author: David Kaplan
Publisher: Guilford Publications
ISBN: 146251667X
Category : Psychology
Languages : en
Pages : 338
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Book Description
This book has been replaced by Bayesian Statistics for the Social Sciences, Second Edition, ISBN 978-1-4625-5354-9.
Author: Thad Dunning
Publisher: Cambridge University Press
ISBN: 1107017661
Category : Business & Economics
Languages : en
Pages : 379
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Book Description
The first comprehensive guide to natural experiments, providing an ideal introduction for scholars and students.
