How Do You Synthesize Studies Conducted in Very Different Settings?

What If the Studies Ask Similar Questions in Very Different Settings?

You may find several studies investigating essentially the same phenomenon but under conditions that look remarkably different. One study takes place in a large urban university, another in a small rural college, another in a fully online institution, and another in a national system with different policies, resources, and institutional practices.

The temptation is to treat setting as background information. After all, the research question may be similar, the variables may have familiar names, and the findings may appear directly comparable.

But settings are not simply addresses attached to studies. They can shape what participants experience, which resources are available, how an intervention is implemented, what comparison conditions mean, and whether the same mechanism has an opportunity to operate.

The synthesis problem is therefore not whether different settings can be combined. They often can. The question is whether the findings remain meaningfully comparable once the contextual conditions under which they were produced are taken seriously.

Synthesize the Pattern Across Settings Without Erasing the Context

Setting Is Part of the Evidence, Not Merely Where the Study Happened

What counts as a research setting?

A setting is the environment or system within which a study occurs. Depending on the research question, this might refer to a school, university, hospital, workplace, community, laboratory, online platform, country, health system, or another institutional or social environment.

More importantly, setting includes characteristics of that environment that may influence the phenomenon being studied. Two universities, for example, are not necessarily equivalent merely because both can be labeled “higher education.” They may differ substantially in class size, infrastructure, admissions, instructional practices, institutional resources, student support, technology access, policies, and organizational culture.

Systematic-review guidance recognizes this broader problem as part of study diversity. Cochrane distinguishes clinical diversity, including differences in participants, interventions, and outcomes, from methodological diversity and statistical heterogeneity. Whether diversity matters depends on whether the varying characteristics could affect the phenomenon or effect being synthesized.

Do not treat country as a complete description of context

Country is often used as shorthand for setting because it is easy to extract and report. Sometimes national context genuinely matters. Regulatory systems, educational structures, health systems, economic conditions, languages, infrastructure, and cultural practices may differ across countries in ways relevant to a research question.

But “Study A was conducted in Country X” is not an explanation of context.

Studies conducted in the same country can take place in profoundly different environments. Conversely, institutions in different countries can share important structural characteristics. Geography can therefore be informative without being the causal explanation for every difference.

Ask which contextual features could plausibly matter

Not every setting characteristic deserves equal attention. A useful synthesis focuses on contextual features with a plausible relationship to the phenomenon under investigation.

Depending on the topic, these might include:

• institutional resources and infrastructure;
• urban, rural, or remote conditions;
• delivery mode, such as face-to-face, hybrid, or online;
• organizational policies and procedures;
• staffing levels or professional expertise;
• class, clinic, workplace, or group size;
• technology access and reliability;
• institutional selectivity or specialization;
• health, educational, or regulatory systems;
• implementation support and local practices;
• economic or material constraints;
• existing services or comparison conditions.

The relevant dimensions should emerge from the research question and the evidence rather than from a generic checklist. Otherwise, contextual analysis can become another elaborate spreadsheet that everyone admires and nobody uses.

Separate setting from population

Setting and population frequently travel together, but they answer different questions.

A study of nurses working in a public hospital and another involving similar nurses in private community clinics may involve related populations but substantially different settings. Conversely, one university may contain undergraduate, postgraduate, domestic, and international populations within the same institutional setting.

Keeping these dimensions separate helps prevent contextual explanations from being confused with differences between study populations.

Setting can alter what an intervention actually means

Suppose several studies evaluate “online tutoring.” In one institution, students receive high-speed internet, trained tutors, integrated learning-management-system support, and scheduled sessions. In another, tutoring occurs through basic messaging applications because connectivity is unreliable.

The intervention label is the same, but its practical realization differs.

This is why context can influence implementation. Resources, infrastructure, organizational support, workload, incentives, policies, and existing practices may change how an intervention is delivered and experienced.

A finding that differs across settings may therefore reflect contextual variation in implementation rather than an intrinsically different intervention effect. The synthesis should preserve that possibility.

The comparison condition can also change across settings

Researchers often concentrate on whether the focal intervention is comparable while overlooking what participants would otherwise receive.

Imagine that an intervention is compared with “usual practice.” Usual practice may be highly developed in one institution and minimal in another. Even if the intervention itself is identical, its incremental benefit could differ because the baseline comparison differs.

Context therefore matters on both sides of a comparison.

Look for patterns of contextual dependence

If findings differ across settings, avoid stopping at “results were inconsistent.” Ask whether the variation follows an interpretable contextual pattern.

Perhaps an intervention works consistently in settings with strong implementation support but not where staff receive little training. Perhaps an association appears in face-to-face environments but becomes weaker in fully online contexts. Perhaps effects are larger where the comparison condition provides fewer existing services.

These patterns can help transform conflicting findings into a more informative synthesis.

However, observational comparisons across studies require caution. If studies conducted in different settings also use different populations, measures, designs, and procedures, setting cannot automatically be identified as the reason their findings differ. Cochrane guidance similarly cautions that investigations of heterogeneity, particularly those developed after seeing the results, may generate useful hypotheses but should be interpreted cautiously.

Consistency across different settings can broaden the evidence

Contextual diversity can also be informative when findings converge.

If a similar relationship appears across institutions with different resources, delivery systems, or organizational structures, the evidence is less dependent on one particular environment. This does not establish universal generalizability, but it may support a broader conclusion than repeated studies conducted under nearly identical conditions.

The wording matters. “The pattern was observed across the settings studied” is defensible. “The finding applies in any setting” usually is not.

Setting differences can create indirectness

Evidence can be methodologically strong yet only indirectly answer the question you care about. Cochrane's guidance on applicability describes indirectness as arising when the available studies address a restricted version of the target question, including differences in population, intervention, comparator, or other relevant conditions.

Suppose you want to know whether an intervention works in resource-constrained public universities, but nearly all available studies come from highly resourced institutions. Those studies may be rigorous. The difficulty is not necessarily their internal quality. It is whether their evidence transfers adequately to your target setting.

This distinction is important because “high-quality evidence” and “directly applicable evidence” are not synonymous.

Do not average away meaningful contextual variation

Statistical pooling or a narrative majority count can produce an overall conclusion even when effects vary substantially across settings. An average can be useful, but it does not explain the variation.

Cochrane recommends considering heterogeneity when interpreting synthesized results and notes that a meta-analysis may be misleading when variation is considerable, particularly when effects differ in direction. PRISMA likewise recommends reporting how studies were grouped for synthesis and how possible causes of heterogeneity were investigated.

Sometimes context is the finding

A literature review need not end with one context-free statement about whether something “works.” A more informative conclusion may identify the conditions under which a pattern appears stable and those under which it changes.

That approach is especially valuable in literatures where almost every study is different. Contextual heterogeneity stops being clutter to remove and becomes part of what the literature teaches you.

When the Same Intervention Enters Four Different Institutional Worlds

Common Mistakes When Research Settings Differ

Ask Where the Finding Holds and Under What Conditions

When settings differ, your synthesis should preserve enough context to determine whether those differences help explain the evidence. That does not mean writing miniature descriptions of every institution. Focus on contextual characteristics that could plausibly change the phenomenon or its interpretation.

Then examine whether findings travel across those differences.

This can reveal what the literature still cannot answer. Sometimes a field has repeatedly established that something works under one set of conditions while barely testing whether the conclusion survives outside them.

Before Combining Studies From Different Settings, Check:

Questions About Synthesizing Different Research Settings

Context Should Refine the Pattern, Not Disappear From It

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