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.
Macartan (Wissenschaftszentrum Berlin fur Sozialforschung) Humphreys & Alan M. (University of British Columbia, Vancouver) Jacobs
Integrated Inferences [PDF ebook]
Causal Models for Qualitative and Mixed-Method Research
Integrated Inferences [PDF ebook]
Causal Models for Qualitative and Mixed-Method Research
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格式 PDF ● ISBN 9781316766880 ● 出版者 Cambridge University Press ● 发布时间 2024 ● 下载 3 时 ● 货币 EUR ● ID 9657787 ● 复制保护 Adobe DRM
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