Dr Jason Seawright is an Associate Professor of Political Science at Northwestern University. He is the author of two books, Party-System Collapse: The Roots of Crisis in Peru and Venezuela (Stanford, 2012) and Multi-Method Social Science: Combining Qualitative and Quantitative Tools (Cambridge, 2016). He has also published in Political Analysis, Sociological Methods and Research, the American Journal of Political Science, Perspectives on Politics Comparative Political Studies, among other journals and edited volumes. His research interests include causal inference, mixed-methods research designs, political parties and party systems, populism, and political representation.

Course Content
The course will discuss mixed-method research in the sense of research designs that incorporate quantitative as well as case-study components. It will begin by analyzing epistemological arguments in favour of mixed-method research, as well as obstacles to success in such research. Then it will consider the comparative strengths of qualitative and quantitative modes of analysis. Three broad strategies for mixed-method analysis will be explored: using statistics to select cases for qualitative analysis; designing and implementing case-study research to extend prior quantitative findings; and revising statistical analysis to test, incorporate, or extend case-study insights. Finally, three specific mixed-method designs will be considered in depth: the paired comparison framed by prior regression analysis; the quantitative comparative case study; and experiments nested within case studies. The central methodological challenge of the course is how to manage causal inference using inferential tools with fundamentally different sets of assumptions.

Course Objectives
Participants should expect to develop an increased ability to notice and take advantage of aspects of the theories or hypotheses that they test which are open to consideration using multiple research designs. They will also possess a toolkit of statistical techniques and case-study ideas that facilitate successful multi-method research. These tools and abilities are particularly useful in planning and executing coherent large-scale research projects, such as book-length work in which multiple research components are intended to speak to a common underlying theory or question.

Course Prerequisites
Students should have carried out regression analysis using real social scientific data at least a few times. They should be able to read and interpret the standard regression tables routinely published in social science journals. They should also have read at least some instances of case-study research in the social sciences.

Representative Background Reading
Lieberman, Evan S. 2005. Nested analysis as a mixed-method strategy for comparative research. American Political Science Review 99 (3): 435-52.

Required texts Please note this book will be provided to registered participants on arrival.
Seawright, Jason. Multi-Method Social Science: Combining Qualitative and Quantitative Tools (Cambridge, 2016). ISBN: 978-1107483736

Provisional course programme (emphasis given to topics may change)

1) The epistemological argument for mixed-method designs
2) Comparative strengths of qualitative research techniques
3) Quantitative tools for case selection
4) Designing and implementing case studies in light of prior quantitative research
5) Revising statistical analysis to test/incorporate case-study insights
6) A specific design: a paired comparison framed by prior regression analysis
7) A specific design: the quantitative comparative case study
8) A specific design: experiments nested within case studies

Day 1: Epistemology and Mixed-Method Designs
• What are mixed-method designs (examples)?
• Characterising methods in terms of assumptions and inferential scope.
• Arguments for and against mixed-method designs.

Day 2: Comparative Strengths and Weaknesses
• Strengths and weaknesses of qualitative research
• Varieties of quantitative research, with strengths and weaknesses
• Strengths and weaknesses of experiments

Day 3: The Comparative Method and Case Selection
• Mill’s methods
• Crucial cases
• Quantitative tools for case selection

Day 4: Case Studies after Quantitative Work I
• Causal mechanisms and regression models
• Measurement error

Day 5: Case Studies after Quantitative Work II
• Discovering and analysing omitted variables
• Considering dependency among cases
• Thinking about interactions

Day 6: Quantitative Work after Case Studies
• Testing generalizability
• Mechanisms and models
• Addressing measurement problems
• Testing the “importance” of omitted variables

Day 7: A specific design: a paired comparison framed by prior regression analysis
• Nested induction/inference
• Examples

Day 8: A specific design: the quantitative comparative case study
• Comparative method and comparative within-country quantitative analysis
• Strengths and weaknesses vis-à-vis pooled quantitative analysis, hierarchical models, and qualitative case studies
• Examples

Day 9: A specific design: experiments nested within case studies
• Theory, case studies, and experiments
• Basics of experimental design
• Qualitative evidence and external validity
• Incorporating experimental results in the case study

Day 10: Matching methods and case studies
• Basics of quantitative matching methods
• Using case studies to evaluate causal inferences based on quantitative matching methods
• Matching to choose cases for qualitative research