myAriel

Case studies are a specific type of research design in the social sciences. They are an attractive choice for researchers as they allow for covering a wide variety of social and political phenomena with a great dose of detail. Case study research has been a powerful tool for developing, refining, and testing social scientific theories and producing research findings useful to policymakers. This version of the Data Lab aims to explore case study research's main components and examine its place within social science methodology. Case studies can serve a wide variety of research goals, from thick descriptions of particular cases to developing and testing causal theories, yet each goal requires specific types of designs. We will focus on two types of designs that are most prevalent in contemporary social and political sciences: (i) cross-case comparisons (controlled comparisons), focusing on identifying factors and conditions that are credibly associated with an outcome of interest; and (ii) within-case analysis (process tracing) focusing on tracing causal mechanisms linking those factors and conditions to outcomes.

 The course has the two-fold objective of understanding the advantages and challenges of these different designs and acquiring the essential tools to conduct rigorous case study research. We will explore both the theory and practice of cross-case and within-case analysis, focusing on core issues such as appropriate case selection techniques, the type of data that we can use (e.g., testimonial, observation, archival), the types of inferences that we can make based on these data, and strategies to assess the quality and evidentiary weight of these data.

At the end of the course, students will have the tools to be both critical consumers of literature using case study research and start becoming aware producers of that literature.