Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research (Strategies for Social Inquiry) - Softcover
Synopsis
Develops a new approach to the use of causal models for qualitative and mixed-method research design and causal inference.
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About the Authors
Macartan Humphreys is Professor of Political Science at Columbia University and Director of the Institutions and Political Inequality group at the WZB Berlin, conducting research on post-conflict development, ethnic politics, and democratic decision-making. He has been President of the APSA Experimental Political Science section and Executive Director of the Evidence on Governance and Politics network.
Alan M. Jacobs is Professor of Political Science at the University of British Columbia, conducting research on comparative political economy in democratic settings. He has been President of the APSA's Qualitative and Multi-Method Research section, winner of the section's Mid-Career Achievement Award, and a regular instructor at the Institute for Qualitative and Multi-Method Research.
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Integrated Inferences : Causal Models for Qualitative and Mixed-method Research
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Paperback. Condition: new. Paperback. There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question. Introduces a Bayesian approach to the use of causal models to design and carry out qualitative and mixed-methods research. Addressed to researchers across the social sciences, this book shows how causal models allow us to combine extensive and intensive data strategies to answer both general and case-specific causal questions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781316620663
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Paperback. Condition: New. There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question. Seller Inventory # LU-9781316620663
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Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research (Strategies for Social Inquiry)
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