Case Study Analysis I

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 with the foundation for engaging in traditional case study analysis. The analytical focus is on making inferences about causal relationships when you have a small number of cases. We explore the differences between case study and quantitative analysis, concepts and measurement, and the importance of case selection prior to engaging in case study analysis. The specific case study methods we will learn include structured, focused comparison for use with single cases and cross-case analysis, and several forms of cross-case comparison. Participants will have many opportunities to practice the application of these methods in class. This course is ideal if you wish to understand the basic principles of research design and their application to case study analysis.

This one-week course is the first part in a two-course sequence (cf. Case Study Analysis II) and provides the foundation for more advanced qualitative and mixed methods courses, such as Mixed Methods and Qualitative Data Analysis I & II.

Dates

This one-week, 17.5-hour course runs Monday-Friday, July 3-7, 2023. The course is scheduled for 9:00 am-12:30 pm.

Cameron G. Thies, Arizona State University

Detailed Description

This qualitative methods course reviews basic principles of research design to prepare participants to conduct case study analysis. It also provides participants with an introductory set of methodological tools to use case study methods in pursuit of causal inference when we are analyzing a small number of cases. We therefore focus 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.

This introductory course begins with a justification for using case study approaches for causal explanation in comparison to quantitative analyses of large numbers of observations. Participants will learn the strengths and weakness of both case study and quantitative analysis, and how they may complement each other. We also review different approaches to understanding and establishing causality, including causal mechanisms, counterfactuals, experimental, and neo-Humean regularity. Case study methods draw on multiple forms of establishing causality, as we will learn in this course and Case Study Analysis II. We move on to a discussion of the importance of concepts and measurement in case studies. Given that we are dealing with a small number of cases, our concepts can be richer and more complex, even as our measurement tends to be simpler. We will also study the critical concern of case selection. Given that we will analyze a single case or compare a small number of cases, the selection of cases and the justification we provide for that selection is critical to the inferences we draw and how other people evaluate our work. Additionally, case selection often comprises the method in many case study methods.

After covering the aforementioned basic issues of research design for case study analysis, we move on to learn some specific methods. We introduce the method of structured, focused comparison as a prototype for case study analysis. This method is essentially a way of structuring the analysis of each case, whether your focus is a single case or you intend to compare multiple cases. The method of structured, focused comparison can be combined with any of the other methods we describe. Our final session is devoted to cross-case comparisons. We will learn several versions of Mill's Methods, often known as the comparative method, and explore several other types of cross-case comparison.

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.

Prerequisites

There are no prerequisites for this course.

Requirements

Participants are expected to have access to an internet-connected computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.

Core Readings