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Integrated Inferences : Causal Models for Qualitative and Mixed-Method Research by Alan M. Jacobs and Macartan Humphreys (2023, Trade Paperback)

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Integrated Inferences : Causal Models for Qualitative and Mixed-method Research, Paperback by Humphreys, Macartan; Jacobs, Alan M., ISBN 1316620662, ISBN-13 9781316620663, Brand New, Free shipping in the US

About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101316620662

ISBN-139781316620663

eBay Product ID (ePID)13059331296

Product Key Features

Book TitleIntegrated Inferences : Causal Models for Qualitative and Mixed-Method Research

Number of Pages300 Pages

LanguageEnglish

Publication Year2023

TopicResearch

GenreSocial Science

AuthorAlan M. Jacobs, Macartan Humphreys

Book SeriesStrategies for Social Inquiry Ser.

FormatTrade Paperback

Dimensions

Item Height1 in

Item Length9.6 in

Item Width6.7 in

Additional Product Features

Dewey Edition23

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 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