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Product Identifiers
PublisherCambridge University Press
ISBN-101107169623
ISBN-139781107169623
eBay Product ID (ePID)4059331086
Product Key Features
Book TitleIntegrated Inferences : Causal Models for Qualitative and Mixed-Method Research
Number of Pages300 Pages
LanguageEnglish
TopicResearch
Publication Year2023
GenreSocial Science
AuthorAlan M. Jacobs, Macartan Humphreys
Book SeriesStrategies for Social Inquiry Ser.
FormatHardcover
Dimensions
Item Height1 in
Item Length9.8 in
Item Width6.9 in
Additional Product Features
LCCN2023-036421
Reviews'An ambitious attempt to understand and test qualitative theories in social science by embedding their design and analysis into a quantitative Bayesian framework, this book will give economists, political scientists, and other researchers a lot to chew on for years to come.' Andrew Gelman, Professor of Statistics and Political Science, Columbia University
Dewey Edition23
Dewey Decimal001.42
Table Of Content1. Introduction; I. Foundations: 2. Causal models; 3. Illustrating causal models; 4. Causal queries; 5. Bayesian answers; 6. Theories as causal models; II. Model-based causal inference: 7. Process tracing with causal models; 8. Process tracing applications; 9. Integrated inferences; 10. Integrated inferences applications; 11. Mixing models; III. Design choices: 12. Clue selection as a decision problem; 13. Case selection; 14. Going wide, going deep; IV. Models in question: 15. Justifying models; 16. Evaluating models; 17. Final words; V. Appendices: 18. Causal Queries; 19. Glossary; Bibliography; Index.
SynopsisIntroduces 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., 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.
LC Classification NumberHM791
