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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Bestandsnummer des Verkäufers 1107169623-8-1
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Bestandsnummer des Verkäufers ria9781107169623_new
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Bestandsnummer des Verkäufers 45804874-n
Anzahl: 2 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 45804874-n
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Hardcover. Zustand: new. Hardcover. 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. Bestandsnummer des Verkäufers 9781107169623
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 45804874
Anzahl: 2 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 300 pages. 9.61x6.69x0.94 inches. In Stock. This item is printed on demand. Bestandsnummer des Verkäufers __1107169623
Anzahl: 1 verfügbar
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
Hardback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 975. Bestandsnummer des Verkäufers C9781107169623
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 45804874
Anzahl: 2 verfügbar
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Hardcover. Zustand: new. Hardcover. 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781107169623
Anzahl: 1 verfügbar