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Tasha Fairfield

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London School of Economics

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London School of Economics
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Tasha Fairfield is Associate Professor in the Department of International Development at the London School of Economics. She holds a Ph.D. in political science from the University of California, Berkeley, along with an M.A. in Latin American Studies and an M.S. in physics from Stanford University. Her first book, Private Wealth and Public Revenue in Latin America: Business Power and Tax Politics (CUP 2015), won the Donna Lee Van Cott Award. Her methodology articles include “Explicit Bayesian Analysis for Process Tracing” (Political Analysis 2017, with A.E. Charman), which won the American Political Science Association’s Qualitative and Multi Method Research Sage Best Paper Award. She was a 2017-18 Mellon Foundation Fellow at the Center for Advanced Study in Behavioral Sciences at Stanford University. Her book (with A.E. Charman), Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, is forthcoming with Cambridge University Press.

Courses taught by this instructor

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B = Basic
M = Intermediate
A = Advanced

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B = Basic
M = Intermediate
A = Advanced

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Qualitative Bayesian Reasoning for Case Studies

The way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred— whether a major tax reform in Chile, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of policy change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we continually update our beliefs about which hypothesis provides the best explanation—or we may introduce a new alternative that occurs to us along the way. Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis—e.g., “the imperative of attracting globally-mobile capital motivated policymakers to reform the tax system,” or “a lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled”—given our relevant prior knowledge and new information that we obtain during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative process-tracing research. This interactive course introduces the principles of Bayesian reasoning for process tracing and case study research with the goal of helping to leverage common-sense understandings of inference and hone intuition when conducting causal analysis with qualitative evidence. We will examine concrete applications to single case studies, comparative case studies, and multi-methods research. Participants will learn how to construct rival hypotheses, assess the inferential weight of qualitative evidence, and evaluate which hypothesis provides the best explanation through Bayesian updating. The short course will also overview key aspects of research design, including iteration between theory development and data analysis. Throughout, we will conduct a wide range of exercises and group work to give participants hands-on practice at applying Bayesian techniques. Upon completing the course, participants will be able to read qualitative case studies more critically and apply Bayesian principles to their own research.
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M

Qualitative Bayesian Reasoning for Case Studies

The way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred— whether a major tax reform in Chile, or the death of Samuel Ratchett on the Orient Express—drawing on the literature we have read (e.g. theories of policy change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we continually update our beliefs about which hypothesis provides the best explanation—or we may introduce a new alternative that occurs to us along the way. Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis—e.g., “the imperative of attracting globally-mobile capital motivated policymakers to reform the tax system,” or “a lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled”—given our relevant prior knowledge and new information that we obtain during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative process-tracing research. This interactive course introduces the principles of Bayesian reasoning for process tracing and case study research with the goal of helping to leverage common-sense understandings of inference and hone intuition when conducting causal analysis with qualitative evidence. We will examine concrete applications to single case studies, comparative case studies, and multi-methods research. Participants will learn how to construct rival hypotheses, assess the inferential weight of qualitative evidence, and evaluate which hypothesis provides the best explanation through Bayesian updating. The short course will also overview key aspects of research design, including iteration between theory development and data analysis. Throughout, we will conduct a wide range of exercises and group work to give participants hands-on practice at applying Bayesian techniques. Upon completing the course, participants will be able to read qualitative case studies more critically and apply Bayesian principles to their own research.
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