Case Study Analysis   

The course was very useful, especially the numerous opportunities to discuss our own research projects in relation to the methods taught each day. — participant from the U.S.

This course provides participants interested in making inferences about causal relationships with a set of methodological tools for case study analysis. We discuss the strengths and limitations of a variety of small-n methods with a focus on the types and scope of causal inference that they allow, paying special attention to the important role of case selection in the application of these methods. The course also examines how case study methods can be nested into mixed methods designs to improve our confidence in causal inferences.

This course provides the foundation for more advanced qualitative and mixed methods courses, such as Qualitative Data Analysis I & II.


This two-week, 35-hour course runs Monday-Friday, 9:00 am-12:30 pm, July 1-12, 2019.


Cameron G. Thies (picture), Arizona State University

Detailed Description

This qualitative methods course provides participants with a set of methodological tools that enable them to use case study methods in pursuit of causal inference. It focuses on the strengths and limitations of different small-n methods aimed at establishing causality. We study the types and scope of inference that are possible with these methods and learn how small-n methods can be nested into mixed methods research designs.

The course begins with an overview of different approaches to understanding and establishing causality, including causal mechanisms, counterfactuals, frequentist versus Bayesian logics of inference, and comparative logics. After exploring these causal logics in the abstract, we explore and practice the methods associated with each of these logics. These methods include various case study methods, such as cross-case, comparative research designs (e.g., Mill's methods, structured-focused comparisons, and typological theorization) and within-case methods (e.g., congruence-matching, pattern-matching, and process tracing). We also discuss the important issue of case selection.

The final part of the course turns to the appropriateness of incorporating case study methods into mixed method research designs. We explore the debate over whether there is a divide between quantitative (large-n) and qualitative (small-n) methods, and whether such a divide can be bridged. Participants will learn how to use quantitative techniques, such as matching, to select cases for their qualitative analyses.

By providing an introduction to the tools and concepts of qualitative research, this course also functions as a 'launch pad' for the more advanced qualitative and mixed methods courses. Participants are not assumed to have any specific prior knowledge of qualitative data analysis or methods of causal inference.


There are no prerequisites for this course.


Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.

Core Readings

George, Alexander L., and Andrew Bennett. 2005. Case Studies and Theory Development in the Social Sciences. Cambridge, MA: MIT Press.

Suggested Readings

Box-Steffensmeier, Janet M., Henry E. Brady, and David Collier, eds. 2010. The Oxford Handbook of Political Methodology. Oxford: Oxford University Press.

Rohlfing, Ingo. 2012. Case Studies and Causal Inference: An Integrative Framework. New York, NY: Palgrave Macmillan.

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