Full Description
Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.
Contents
1. Integrative multi-method research; 2. Causation as a shared standard; 3. Using case studies to test and define regressions; 4. Case selection after regression; 5. Combining case studies and matching; 6. Combining case studies and natural experiments; 7. Embedding case studies within experiments; 8. Multi-method case studies; Appendix A. Qualitative causal models and the potential outcomes framework; Bibliography.
