Case Study Research

In-depth study of a case in its context

Case study research is a qualitative design that investigates one or a few cases in depth within their real-world context, drawing on multiple data sources. It is particularly well-suited to answering 'how' and 'why' questions about contemporary phenomena that the researcher cannot control. Rigour is achieved through clear case definition, triangulation, and analytic—rather than statistical—generalization.

Defining the Concept

Case study research seeks to understand a bounded unit—an individual, group, organization, program, or event—in its natural setting and in relation to its context. In Yin's canonical formulation, the design is appropriate when the researcher has no control over variables, the boundary between phenomenon and context is blurred, and evidence comes from multiple sources. It is not an experimental design; its goal is theoretical, not statistical, generalization.

Types and Key Steps

Yin identifies three core types: descriptive, exploratory, and explanatory (causal). Cases may be single or multiple; embedded designs nest more than one unit of analysis within a single case. The research process involves: defining the research question, bounding and selecting the case(s), constructing a data collection protocol (interviews, observations, documents, archival records), analysing data through pattern matching or explanation building, and triangulating across evidence sources to strengthen validity.

Applied Example

A researcher studying a university's transition to distance education might select a single faculty as the case. Interviews with academic staff, administrative minutes, student feedback, and learning-platform usage logs are analysed together to understand 'how' the transition unfolded. The researcher grounds findings in a theoretical frame—such as institutional change theory—and makes an analytic generalization that claims transferability to similar contexts, not universal statistical representativeness.

Common Pitfalls and Good Practice

The most common misconception is that findings from a single case can be generalized statistically to a population; case study research generalizes analytically to theory, not numerically to populations. A second pitfall is leaving the case boundary undefined: the researcher must clarify at the outset what is 'inside' and 'outside' the case. Developing a case protocol, using multiple sources, and testing rival explanations strengthens internal validity. Researcher bias arising from familiarity with the site should be managed through reflexive journaling.

Key terms

Analytic Generalization: Extending findings to theory rather than to a population; the alternative to statistical generalization.
Triangulation: Using multiple data sources, methods, or investigators to corroborate a finding.
Embedded Design: A case study arrangement with more than one unit of analysis nested within a single case.
Case Protocol: A research instrument documenting data collection procedures and questions to enhance reliability.
Transferability: The potential for findings to apply to similar contexts; the qualitative equivalent of external validity.