Multi-Method Social Science: Combining Qualitative and Quantitative ToolsReflecting 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. |
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Multi-Method Social Science: Combining Qualitative and Quantitative Tools Jason Seawright Limited preview - 2016 |
Multi-Method Social Science: Combining Qualitative and Quantitative Tools Jason Seawright No preview available - 2016 |
Common terms and phrases
Angrist approach argue argument as-if random average case-selection rule case-specific case-study research causal effect causal heterogeneity causal inference causal path causal pathways causal process causal-process observations central chapter Collier comparison components confounding variables connected context contribute control group control variables counterfactual defiers dependent variable deviant deviant-case selection discussed example experimental realism extreme values extreme-case selection finding focus focused Furthermore goals hypotheses important independent instrumental variables integrative multi-method designs involves issues kind Mahoney majoritarian manipulation matching measurement error methods multi-method research design natural experiments omitted variables outcome variable pathway variable Political possible potential outcomes potential-outcomes framework process-tracing propensity scores proportional representation qualitative and quantitative qualitative methods qualitative research quantitative analysis quantitative causal randomly assigned regarding regression analysis regression-type relationship relevant sample scenario scholars scores similar social sciences sources of measurement statistical studies SUTVA techniques theory treatment assignment treatment effect treatment group treatment of interest treatment variable