When people search for “case study examples,” they’re usually looking for one of two very different things: business case studies (customer success stories used in marketing) or research case studies (a formal qualitative design used to investigate a bounded, real-world phenomenon in depth). This guide covers the second sense. It assumes you already understand roughly what a case study is; if you need the full methodological grounding first — what qualifies as a case study, when to choose the method, and how single- and multiple-case designs work — see CASRAI’s Case Study Research Method guide. If you’ve already designed your study and need to write it up, see How to Structure a Case Study Paper in Social Science Research.
What follows are worked, illustrative examples of each major case study type, showing how the research question, case-selection rationale, data sources, and conclusions differ across types.
Case Study Examples by Type
Case study designs are usually classified along two independent dimensions: Yin’s typology, which groups cases by the kind of question they answer (exploratory, descriptive, explanatory), and Stake’s typology, which groups cases by why the case was chosen (intrinsic, instrumental, collective). A single study is typically described using one label from each dimension — for example, an “explanatory instrumental” case study. The examples below are organized by type, with the research question, case-selection rationale, data sources, and what the design could and could not conclude made explicit for each.
Intrinsic Case Study
Illustrative scenario: A researcher studies a single rural hospital that has sustained an unusually low staff turnover rate for over a decade, not because the hospital is thought to represent hospitals generally, but because the researcher’s funding body specifically wants to understand this one institution’s internal culture and history.
- Research question: How has this specific hospital sustained low turnover over time?
- Case selection rationale: The case was chosen for its own sake — the client organization, not the researcher, defined the boundaries of interest.
- Data sources: Staff interviews across tenure lengths, internal HR records, meeting minutes, and direct observation of shift handovers.
- What the design can and cannot conclude: It can produce a rich, internally valid account of this hospital’s specific practices and history. It cannot claim these practices would reduce turnover at a different hospital — that would require an instrumental or collective design built for comparison.
Instrumental Case Study
Illustrative scenario: A researcher studying how mid-sized nonprofits adopt outcome-measurement systems selects one nonprofit that recently completed such an adoption, using it as a lens onto the broader question rather than as an end in itself.
- Research question: What organizational conditions enable outcome-measurement adoption in resource-constrained nonprofits?
- Case selection rationale: The organization was chosen because it is a reasonably typical instance of the broader population the researcher wants to say something about, not because of any unique quality of the organization itself.
- Data sources: Interviews with program staff and leadership, adoption-process documents, and board meeting records covering the adoption period.
- What the design can and cannot conclude: It can generate a theoretically grounded account of adoption conditions that plausibly extends to similar organizations (analytic generalization). It cannot establish how common those conditions are across the population (statistical generalization) — that would require a survey of many nonprofits.
Collective (Multiple-Case) Case Study
Illustrative scenario: A researcher examines four university technology-transfer offices that each restructured their licensing process within the same two-year period, comparing what changed and why across all four.
- Research question: What common mechanisms explain why some technology-transfer restructurings increased licensing volume and others did not?
- Case selection rationale: Four offices were selected to represent variation in institution size and prior licensing volume, following replication logic — each case is treated as a separate test of the same emerging theoretical proposition, not as a sample drawn to represent a population.
- Data sources: Licensing-volume records before and after restructuring, interviews with office directors, and internal process documentation from each of the four offices.
- What the design can and cannot conclude: Consistent findings across the four cases strengthen confidence that the mechanism is real (literal replication) or clarify the conditions under which it operates differently (theoretical replication). The design cannot produce a population-level estimate of how many technology-transfer offices the mechanism affects.
Explanatory Case Study
Illustrative scenario: A researcher investigates why a specific municipal open-data initiative succeeded in achieving sustained public use when several comparable initiatives in similar cities stalled after launch.
- Research question: Why did this initiative achieve sustained public engagement where comparable initiatives did not?
- Case selection rationale: The case was chosen because it is a positive deviant — an outcome that departs from what similar cases produced, making it useful for tracing a causal chain that a survey of many cities couldn’t isolate.
- Data sources: Interviews with initiative staff and civic users, platform usage analytics, city council records, and comparison documentation from two of the stalled initiatives.
- What the design can and cannot conclude: It can trace a plausible causal chain specific to this case, explaining the presumed causal links between specific design choices and sustained use. It cannot establish that the same choices would cause the same outcome in a city with a different starting context — the claim is causal-within-case, not causal-across-population.
Exploratory Case Study
Illustrative scenario: A researcher conducts a preliminary study of one research team’s use of a newly released electronic lab notebook system, before designing a larger, hypothesis-driven study of adoption across many labs.
- Research question: What are the observable dimensions of how a lab actually incorporates a new electronic lab notebook into daily practice?
- Case selection rationale: The case was an early-adopting lab, selected for accessibility and willingness to participate, precisely because the goal was to generate hypotheses and identify relevant variables, not to test an established theory.
- Data sources: Field observation of lab members during a four-week period, informal interviews, and screenshots of notebook entries showing usage patterns.
- What the design can and cannot conclude: It can surface candidate variables and hypotheses — for example, that entry frequency depends on whether the notebook is accessible from shared instruments — worth testing in a larger study. It cannot itself confirm those hypotheses; exploratory case studies are explicitly a precursor to further research, not a substitute for it.
Descriptive Case Study
Illustrative scenario: A researcher documents, in detail, how one interdisciplinary research center structures authorship decisions across its projects, without attempting to explain why the structure emerged or test any causal claim.
- Research question: What does this center’s authorship decision-making process actually look like, in its real-world context?
- Case selection rationale: The center was selected because detailed documentation of authorship practice in interdisciplinary settings was largely absent from the literature, making a thorough description valuable on its own.
- Data sources: Policy documents, interviews with project leads, and a review of authorship-order decisions across the center’s recent publications.
- What the design can and cannot conclude: It can produce a detailed, contextualized account of practice that other researchers can build on. It does not, by design, explain why the practice takes this form or whether it produces better outcomes than alternatives — those are explanatory questions outside its scope.
Critical Instance Case Study
Illustrative scenario: A researcher selects the one clinical trial site, out of a multi-site trial, that had a documented protocol deviation, specifically to test whether an existing theory about deviation causes holds up against the strongest available test.
- Research question: Does the prevailing theoretical explanation for protocol deviations hold in this specific, well-documented instance?
- Case selection rationale: The site was chosen because it represents a critical test — if the theory doesn’t explain this well-documented case, that’s strong evidence against the theory; if it does, that’s meaningful support, precisely because the case was not cherry-picked to be favorable.
- Data sources: The site’s monitoring reports, deviation logs, and interviews with site staff involved in the deviation.
- What the design can and cannot conclude: A single, well-chosen critical case can confirm, disconfirm, or extend a theoretical proposition with more logical force than an arbitrarily chosen case would. It cannot establish how frequently the mechanism it identifies occurs across other sites.
Single-Case vs. Multiple-Case Design and Replication Logic
The intrinsic example above is necessarily single-case: the whole point is that the case itself, not a comparison, is the object of study. The collective example is necessarily multiple-case, and it illustrates replication logic — the principle, developed by Robert K. Yin, that governs how many cases a multiple-case design needs. Replication logic treats each additional case the way an experimenter treats each additional experimental run: added not to build statistical power the way a larger survey sample would, but to test whether the same theoretical proposition holds (literal replication) or to deliberately vary conditions and see how the outcome changes (theoretical replication). A multiple-case study with three cases that each confirm the same mechanism is making a different, and in some ways stronger, argument than a single case making the same claim — but it is still not a sample, and reviewers who ask “why only four cases and not forty” are usually applying sampling logic to a design that doesn’t use it.
How Case Selection Is Justified
None of the scenarios above chose a case at random, and that’s deliberate — case study rigor depends heavily on being able to explain why this case, and not another, was chosen. The justifications that recur across the examples map onto a small set of standard rationales:
- Typical case — chosen because it represents the ordinary, modal instance of the phenomenon (the instrumental nonprofit example above).
- Deviant or extreme case — chosen because its outcome departs sharply from what similar cases produced, making the mechanism easier to see (the explanatory open-data example above).
- Critical case — chosen because it provides the strongest possible test of an existing theoretical proposition (the critical instance example above).
- Maximum-variation case selection — used in multiple-case designs, where cases are deliberately chosen to differ from each other on relevant dimensions (the collective technology-transfer example, which selected offices varying in size and prior licensing volume), so that any pattern holding across all of them is less likely to be an artifact of one shared characteristic.
Whichever rationale applies, it belongs explicitly in the methods section — not left for a reader to infer. See CASRAI’s Sampling Methods reference for how these case-selection logics relate to, and differ from, probability sampling used in quantitative designs.
Triangulation Across Data Sources
Every scenario above draws on more than one data source — interviews plus documents plus observation, at minimum — and that’s not incidental to the examples; it’s a defining feature of rigorous case study design. Triangulation is the practice of checking whether independent sources of evidence converge on the same conclusion, and it functions in case study research the way replication across labs functions in quantitative work: no single source is treated as sufficient on its own. In the descriptive authorship example above, policy documents alone might describe an intended process that interviews reveal isn’t actually followed in practice — triangulation is what surfaces that gap.
Yin, Stake, and Eisenhardt: Three Traditions, Compared
The examples above are written to be compatible with all three major methodological traditions, but the traditions themselves differ in emphasis, and it’s worth knowing which one a given piece of guidance (or a given reviewer) is drawing on:
- Robert K. Yin treats case study as a formal method with an explicit research design, a case study protocol, and structured evidentiary standards (construct validity, internal validity, external validity, reliability) borrowed and adapted from the language of quantitative design. Yin’s approach is the most procedural of the three and underlies most of the type labels used above (exploratory, descriptive, explanatory; single- vs. multiple-case; replication logic).
- Robert Stake treats case study less as a fixed method and more as a choice of what to study — a bounded system examined as an integrated whole — and emphasizes naturalistic, interpretivist inquiry over standardized procedure. Stake’s intrinsic/instrumental/collective typology, used throughout the examples above, comes from this tradition, as does its greater tolerance for the researcher’s own interpretive voice in reporting findings.
- Kathleen Eisenhardt, writing primarily for organizational and management research, developed a structured approach to building theory from multiple cases: a roadmap running from case selection through within-case analysis, cross-case pattern search, and iterative comparison against emerging and existing theory, with theoretical saturation (rather than a fixed case count) determining when to stop adding cases. The collective/multiple-case example above, and its use of replication logic to build rather than just test theory, sits closest to this tradition.
None of the three is “correct” to the exclusion of the others — the choice depends on the field’s conventions and on how procedural versus interpretive the resulting paper needs to be. Stating explicitly which tradition a study follows, in the methods section, heads off reviewer confusion about why the paper doesn’t follow a different tradition’s conventions.
Validity and Generalizability: Writing About Limits Honestly
Every “what the design can and cannot conclude” line in the examples above is doing the same job: stating the limit of the claim before a reviewer has to point it out. The single most common reviewer objection to case study research is that findings from one or a few cases can’t be generalized — and the honest response, following Yin, is not to deny the limitation but to name the correct kind of generalization being claimed. Case studies generalize analytically, extending or refining a theoretical proposition, not statistically, in the sense of estimating a population parameter. See CASRAI’s Generalizability in Research guide for the full distinction and for how to phrase a generalizability statement in a methods or discussion section without overclaiming or underselling the work.
Ethics and Consent When a Case Is an Identifiable Organization or Person
Several of the scenarios above involve a single, identifiable hospital, nonprofit, research center, or clinical trial site — and that identifiability is itself a design consideration, not just a write-up detail. Unlike a survey respondent who can be reported as one anonymized row among hundreds, a case study organization or individual is often recognizable from the description alone, even without being named. That raises two separate obligations: securing informed consent from participants within the case (interviewees, observed staff) on the same basis as any qualitative study — see CASRAI’s informed consent reference — and, separately, negotiating with the case organization itself about how much identifying detail will appear in the published account, since organizational consent and individual participant consent are not the same thing and an IRB or ethics board will typically expect both to be addressed. Where a case cannot be adequately anonymized without destroying its analytic value, that trade-off should be disclosed to participants and reviewers rather than solved silently by either overclaiming anonymity or omitting the disclosure altogether.
Frequently Asked Questions
What are the main types of case study, and is there one universal example?
No — that’s the point of this guide. The type that best fits a research question (intrinsic, instrumental, collective, explanatory, exploratory, descriptive, or critical instance) determines what kind of example is even relevant, because each type answers a structurally different question. See the worked examples above for each type.
How is a case study example different from a case report?
A case study, in the research-method sense used throughout this guide, is a bounded investigation using multiple triangulated data sources to answer a “how” or “why” question about a phenomenon in its real-world context. A case report, common in clinical fields, is typically a single-patient or single-event write-up documenting an unusual clinical presentation, without the same design apparatus (case-selection rationale, replication logic, triangulated data collection). See CASRAI’s case study research method guide for the fuller distinction.
Can a case study example use quantitative data?
Yes. Case study is a design, not a data type — it commonly combines qualitative sources (interviews, documents, observation) with quantitative ones (usage analytics, financial records, licensing volume, as in the collective example above). What makes it a case study is the bounded, in-depth, triangulated treatment of one or a few cases, not the exclusion of numbers.
How many cases does a multiple-case study need?
There’s no fixed number. Under replication logic, the answer is: enough cases to either demonstrate consistent replication of a finding or to deliberately vary conditions until the theoretical pattern is clear — governed by theoretical saturation, not a target sample size. Most published multiple-case designs use somewhere between two and ten cases, but the justification, not the count itself, is what a methods section needs to defend.
Do case study examples need IRB approval?
If the case involves identifiable people (interviews, observation of staff, patient-level data) or identifiable organizations whose participation carries risk, yes — the same human-subjects and consent obligations that apply to any qualitative study apply here, plus the added consideration of organizational-level consent discussed above.
