03 · What You Need to Know
How to Select and Interpret a Typical Case
What Is a Typical Case in Case Study Research?
A typical case is one selected because it resembles relatively common conditions or patterns within a defined reference population on characteristics relevant to the research question.
For example, a researcher investigating institutional adoption of learning management systems might select a university with an enrollment size, governance structure, technology infrastructure, and implementation history that broadly resemble those of many institutions in a specified sector.
The purpose is not necessarily to estimate population averages. Rather, the researcher may seek to understand how a process unfolds under conditions that are not unusually favorable or unfavorable.
Typicality is relational. A case is typical only in comparison with a specified set of other cases and according to selected dimensions.
Without that reference, describing an institution as typical provides little methodological information.
Why Is a Typical Case Sometimes More Useful Than an Extreme Case?
Extreme cases can reveal unusual mechanisms, intensified problems, or unexpected outcomes. However, the conditions that make them exceptional may limit their direct relevance to ordinary institutional practice.
Suppose a university achieves exceptionally high AI adoption because it has unusually extensive funding, dedicated technical personnel, and a strong institutional mandate.
That case may reveal how implementation works under favorable conditions. It may provide less direct insight into the everyday challenges faced by institutions with ordinary resources.
A typical university could instead reveal how administrators balance limited budgets, faculty workload, technical support, and competing educational priorities.
Neither case is inherently superior. The relevant question is which selection provides better evidence for the research purpose.
The distinction is examined further in selecting extreme or unusual cases for case study research.
What Does Typical Mean Relative to a Reference Population?
A case may be typical within one population but unusual within another.
For example, a medium-sized private university may be typical among private universities with similar academic offerings but not among all higher education institutions nationally.
Likewise, a school with moderate internet connectivity may be typical in one region but unusually well connected in another.
Researchers should therefore define the population or category of cases before identifying typical characteristics.
The reference population might consist of public secondary schools in one administrative region, private universities of a particular size, or institutions that implemented a specific educational technology.
The selected population should follow from the research question rather than be chosen retrospectively to make a preferred case appear typical.
Which Characteristics Should Establish Typicality?
The characteristics used to define typicality should be relevant to the phenomenon being investigated.
For a study of educational technology implementation, relevant dimensions might include institutional resources, technology infrastructure, student enrollment, governance arrangements, faculty digital competence, or implementation maturity.
However, including every available characteristic may create an unnecessarily complicated selection procedure.
The researcher should prioritize dimensions that plausibly influence the process under investigation.
For example, if the research concerns how schools manage digital assessment with limited technical support, the availability of technical personnel may be more relevant than institutional age.
The criteria should be justified substantively and, where possible, supported by reliable comparative information.
Is a Typical Case the Same as an Average Case?
Not necessarily. Average and typical are related concepts, but they should not be treated as interchangeable.
An average case may be selected because its value on a particular numerical measure is close to the population mean.
A typical case may instead be selected because it reflects a common configuration of characteristics, institutional arrangements, or experiences.
For example, a university with average enrollment may still be unusual in its governance, funding, or digital infrastructure.
Similarly, a university with a slightly above-average enrollment may be more typical of the sector's ordinary institutional arrangements.
Researchers should explain whether typicality refers to proximity to a statistical center, membership in a common category, or similarity to a theoretically meaningful configuration.
Can a Case Be Typical on Some Dimensions but Unusual on Others?
Yes. No case is likely to be typical on every conceivable characteristic.
For example, a university may have ordinary enrollment, funding, and governance arrangements but unusually high faculty participation in AI policy development.
Whether the institution qualifies as a typical case depends on which dimensions are central to the research question.
If the study concerns ordinary resource constraints during technology implementation, the institution may be a reasonable choice.
If the study concerns faculty participation in AI governance, its unusually high participation could make it less typical.
Researchers should therefore identify the dimensions on which typicality is claimed and acknowledge consequential differences on other dimensions.
How Can Quantitative Data Support Typical-Case Selection?
Quantitative data can help identify cases with characteristics near the center of a relevant distribution or within a common range.
For example, researchers might examine institutional enrollment, technology expenditure, faculty-student ratios, or digital platform adoption rates.
Descriptive statistics can help establish whether a candidate institution is unusually high, unusually low, or relatively ordinary on selected measures.
However, choosing a case near the mean on one variable does not establish typicality across all relevant dimensions.
Researchers should also examine the distribution itself. In highly skewed populations, the mean may describe few actual institutions well.
Median values, distributional ranges, categorical characteristics, and substantive contextual information may provide a more defensible basis for selection.
Can Qualitative Evidence Establish Typicality?
Yes. Typical-case selection does not necessarily require a large quantitative dataset.
Researchers may use policy documents, institutional reports, preliminary interviews, expert assessments, and existing studies to identify common organizational arrangements or practices.
For example, documentary evidence may indicate that most institutions in a particular sector use a similar committee structure for educational technology decisions.
A university with that arrangement could be considered typical on the governance dimension.
However, expert judgment should not be treated as automatically authoritative. Researchers should document the basis for the judgment and consider whether available evidence contradicts it.
The goal is a transparent and defensible selection argument rather than a claim of perfect representativeness.
Does Selecting a Typical Case Make the Study Representative?
No. Typicality and statistical representativeness are different methodological concepts.
A typical case is purposively selected because it resembles common or theoretically relevant characteristics.
Statistical representativeness generally concerns the relationship between a sample and a target population, often supported through probability sampling and appropriate estimation procedures.
A single typical institution cannot automatically estimate the prevalence of practices across an entire sector.
For example, studying one university with ordinary AI governance arrangements does not establish how frequently particular governance practices occur among all universities.
The study may provide a detailed account of how those arrangements operate within the selected case, but population-level claims require additional evidence.
Can Typical Cases Support Theoretical Generalization?
Yes. A typical case may contribute to theoretical understanding by examining how a proposed process operates under relatively ordinary conditions.
Suppose researchers propose that faculty participation supports the implementation of institutional AI assessment policies.
A typical university may provide an opportunity to examine how participation, governance, and implementation interact without the exceptional conditions present in highly resourced institutions.
The findings may refine the proposed explanation or identify contextual conditions that deserve further investigation.
However, the theoretical contribution depends on the evidence and analytical argument rather than the label typical.
Researchers should distinguish theoretical generalization from statistical generalization when interpreting case-study findings.
When Is a Typical Case Appropriate for an Instrumental Case Study?
An instrumental case study uses a particular case to illuminate a broader issue or theoretical concern.
A typical case may be useful for this purpose when the researcher wants to understand how the broader issue operates under relatively ordinary conditions.
For example, a university with common governance arrangements may be selected to investigate how institutional policy committees respond to generative AI.
The case is not selected primarily because its history is exceptional, but because it offers an opportunity to examine a broader governance question in a familiar institutional configuration.
However, typical and instrumental describe different dimensions. Typicality concerns the case-selection rationale, while instrumental refers to the broader purpose of studying the case.
Researchers should distinguish these concepts when explaining intrinsic, instrumental, and explanatory case-study orientations.
Can a Typical Case Be Used for Explanatory Research?
Yes. Researchers may select a typical case to investigate how or why a phenomenon develops under ordinary conditions.
For example, a study might examine why a university with average resources experienced gradual rather than rapid adoption of a learning analytics system.
The researcher could investigate institutional decision-making, faculty workload, technical support, and implementation history.
The typical-case rationale establishes why the institution is informative, while the explanatory orientation describes the analytical purpose.
However, selecting a typical case does not automatically strengthen causal identification. Explanatory claims still require adequate evidence and consideration of competing explanations.
What Is the Risk of Selecting a Case That Only Appears Typical?
A case may appear ordinary because researchers lack information about its distinctive characteristics.
For example, a university may have average enrollment and technology expenditure but an unusually influential leadership team or distinctive organizational culture.
These features could substantially shape the phenomenon under investigation.
Researchers should therefore examine whether important unmeasured or poorly documented characteristics undermine the typical-case rationale.
Typicality should be treated as a defensible selection claim, not a guarantee that the case is ordinary in every respect.
How Does Typical-Case Selection Relate to Convenience?
A convenient case may also be typical, but convenience does not establish typicality.
Suppose a researcher selects their own university because institutional records and participants are readily accessible.
The institution might be typical of a particular sector, but that claim requires independent evidence.
The researcher should compare the institution with a relevant reference population and explain why its characteristics support the intended inquiry.
Researcher familiarity may also influence interpretation, participant recruitment, and the willingness of colleagues to disclose sensitive information.
The principles of selecting cases without relying solely on convenience remain applicable even when the chosen institution appears ordinary.
When Is a Typical Case the Wrong Choice?
A typical case may be unsuitable when the research question concerns exceptional outcomes, rare processes, or conditions that ordinary institutions do not experience.
For example, investigating how a university responded to an unprecedented AI-related academic integrity incident may require an unusual case rather than a typical one.
Similarly, a study intended to challenge a theoretical proposition under particularly demanding conditions may benefit from a critical case.
A typical case may also be unsuitable when the researcher cannot establish what ordinary conditions mean within the relevant population.
The selection should follow the research purpose rather than a general preference for cases that appear broadly relatable.
How Should Researchers Report Typical-Case Selection?
The methods section should identify the reference population, the characteristics used to define typicality, and the evidence supporting the selection.
Researchers should explain why those characteristics matter for the research question and acknowledge dimensions on which the case differs from the reference population.
If quantitative data were used, the relevant distributions and selection criteria should be reported where feasible.
If qualitative judgments were used, the sources and reasoning behind those judgments should be transparent.
The researcher should also distinguish the case-selection rationale from claims about generalizability.
A defensible typical-case study explains why the case is informative about ordinary conditions without claiming that one case statistically represents every institution.
