While existing qualitative, case-study methods deliver specific explanations, quantitative approaches to causal inference emphasize valid inferences at the expense of explanations. In this book, David Waldner presents a hybrid method drawing on both approaches to ensure that explanations are based on validly inferred causes and to avoid making valid inferences that have limited explanatory power. Qualitative Causal Inference and Explanation integrates a qualitative identification strategy based on graph-theoretic analysis into traditional process-tracing methods by introducing three novel methodological concepts: hypothetical interventions, invariant causal mechanisms, and event-history maps. This new approach provides clear and feasible standards for making valid, unit-level causal inferences. The result is a groundbreaking approach to explaining complex social and political phenomena, one that better avoids false positives while providing explanations that satisfy the criteria of explanatory depth, density, relevance, and unification.
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David Waldner is Associate Professor in the Department of Politics at the University of Virginia where he studies long-term political and economic development using case-study methods. He is the author of State Building & Late Development (Cornell University Press, 1999) and Rethinking the Resource Curse (with Benjamin Smith, Cambridge University Press, 2021), as well as many articles and book chapters on the dynamics of political regime change and qualitative methods.
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Paperback. Condición: new. Paperback. While existing qualitative, case-study methods deliver specific explanations, quantitative approaches to causal inference emphasize valid inferences at the expense of explanations. In this book, David Waldner presents a hybrid method drawing on both approaches to ensure that explanations are based on validly inferred causes and to avoid making valid inferences that have limited explanatory power. Qualitative Causal Inference and Explanation integrates a qualitative identification strategy based on graph-theoretic analysis into traditional process-tracing methods by introducing three novel methodological concepts: hypothetical interventions, invariant causal mechanisms, and event-history maps. This new approach provides clear and feasible standards for making valid, unit-level causal inferences. The result is a groundbreaking approach to explaining complex social and political phenomena, one that better avoids false positives while providing explanations that satisfy the criteria of explanatory depth, density, relevance, and unification. Rich explanations of real-world events require valid causal analysis. This book adapts quantitative causal inference for qualitative research, offering a rigorous framework for case studies. It blends methodological precision with explanatory depth, helping researchers produce compelling, causally sound accounts of social and political phenomena. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781009769570
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Paperback. Condición: New. While existing qualitative, case-study methods deliver specific explanations, quantitative approaches to causal inference emphasize valid inferences at the expense of explanations. In this book, David Waldner presents a hybrid method drawing on both approaches to ensure that explanations are based on validly inferred causes and to avoid making valid inferences that have limited explanatory power. Qualitative Causal Inference and Explanation integrates a qualitative identification strategy based on graph-theoretic analysis into traditional process-tracing methods by introducing three novel methodological concepts: hypothetical interventions, invariant causal mechanisms, and event-history maps. This new approach provides clear and feasible standards for making valid, unit-level causal inferences. The result is a groundbreaking approach to explaining complex social and political phenomena, one that better avoids false positives while providing explanations that satisfy the criteria of explanatory depth, density, relevance, and unification. Nº de ref. del artículo: LU-9781009769570
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