Integrated inferences: causal models for qualitative and mixed-method research

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, upd...

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Bibliographic Details
Main Authors: Humphreys, Macartan (Author), Jacobs, Alan M. (Author)
Format: Electronic eBook
Language:English
Published: Cambridge, United Kingdom ; New York, NY Cambridge University Press 2023
Series:Strategies for social inquiry
Subjects:
Links:https://doi.org/10.1017/9781316718636
https://doi.org/10.1017/9781316718636
https://doi.org/10.1017/9781316718636
https://doi.org/10.1017/9781316718636
https://doi.org/10.1017/9781316718636
Summary: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
Physical Description:1 Online-Ressource (xii, 422 Seiten) Illustrationen
ISBN:9781316718636
DOI:10.1017/9781316718636