Real-world examples of stratified sampling examples in research

Real examples of stratified sampling examples in research

The fastest way to understand stratified sampling is to see it in action. In stratified sampling, a population is divided into meaningful subgroups (strata) and samples are drawn from each subgroup. The goal is to make sure those groups are properly represented in the final sample.

Here are several real examples of stratified sampling examples in research across different disciplines, with enough detail that you could actually model your own study on them.

Health and medical research: stratified sampling in large surveys

Public health is packed with examples of stratified sampling in research, because health outcomes often differ by age, sex, income, or geography.

1. National health surveys using age and sex strata

Consider a national health survey similar to the National Health and Nutrition Examination Survey (NHANES) run by the CDC in the United States (cdc.gov/nchs/nhanes). Researchers know that health risks vary sharply by age and sex, so they don’t want a sample that accidentally overrepresents young adults and underrepresents older adults.

A typical example of stratified sampling in this context works like this:

• The population is divided into age groups (for instance, 18–29, 30–44, 45–64, 65+).
• Each age group is further split by sex (male, female) to create strata.
• Within each stratum, individuals are randomly selected.

This guarantees that older adults, who might be a smaller share of the population but have higher rates of chronic disease, are still well represented. Analysts can then compare blood pressure, diabetes rates, or obesity across age–sex strata with much more precision than with a simple random sample.

2. COVID-19 antibody studies by region and risk group

During the COVID-19 pandemic, many antibody prevalence studies used stratified sampling to avoid biased estimates. A common example of stratified sampling in research from 2020–2024 looks like this:

• Strata defined by region (urban, suburban, rural), age group, and sometimes occupation (healthcare worker vs. other).
• Within each region–age–occupation stratum, participants are randomly invited for antibody testing.

This approach helps researchers estimate how widely the virus has spread in different communities and risk groups. For instance, a 2022–2023 study might compare infection rates among rural older adults and urban young adults. Without stratified sampling, a volunteer sample could heavily overrepresent health-conscious urban professionals and badly mislead public health planning.

If you’re writing a methods section, this kind of design is one of the best examples of stratified sampling examples in research to reference, because it shows how stratification improves both accuracy and fairness across subgroups.

Education studies: stratifying by school type, grade, and location

Education research offers some very clear, real-world examples of stratified sampling examples in research, especially when researchers want to compare different school environments.

3. Comparing test scores across school types

Imagine a state-level study of math achievement in public, charter, and private high schools. If you drew a simple random sample of students across the state, you’d almost certainly end up with far more public school students, just because there are more of them.

A more thoughtful design uses stratified sampling:

• First, classify all schools into strata: public, charter, private.
• Within each stratum, randomly select schools.
• Within each selected school, randomly select students.

Now, even if private schools only educate, say, 10% of students, the study can still include enough private-school students to make statistically meaningful comparisons. This is a textbook example of stratified sampling in research that you’ll see in many education policy reports.

4. National student surveys stratified by region and grade

Large-scale assessments like those conducted by the National Center for Education Statistics (NCES) (nces.ed.gov) often stratify samples by:

• Geographic region (Northeast, Midwest, South, West)
• Urbanicity (urban, suburban, rural)
• Grade level (e.g., 4th, 8th, 12th)

Within each stratum, schools or students are randomly selected. This makes it possible to say, for example, whether 8th graders in rural schools in the Midwest are catching up or falling behind compared with suburban schools in the same region. If you’re looking for real examples of stratified sampling examples in research that are easy to explain to students, NCES survey designs are a great model.

Political polling and social surveys: stratifying by demographics

When you see pre-election polls on the news, you’re almost always looking at some form of stratified sampling.

5. Election polling by region, age, and race

Polling organizations know that voting preferences differ by region, age, race, and education. A standard example of stratified sampling in research for an election poll might:

• Divide the country into regions (e.g., Northeast, South, Midwest, West).
• Create strata by age group (18–34, 35–49, 50–64, 65+).
• Sometimes add another layer, such as race/ethnicity or education level.

Within each stratum, pollsters select phone numbers or online panel participants at random. They may oversample smaller but politically important groups (for instance, young Hispanic voters) and then apply weights in analysis so the final estimates match the population.

This is one of the best examples of stratified sampling examples in research for understanding how stratification and weighting work together.

6. Social attitude surveys with income and gender strata

Social scientists studying attitudes on topics like climate policy or healthcare often worry that lower-income or less-educated participants will be underrepresented if they rely on convenience samples.

A better design uses stratified sampling based on:

• Income brackets (for instance, <\(40k, \)40k–\(79k, \)80k+)
• Gender (male, female, non-binary/other, where data collection allows)

Researchers then randomly select respondents within each income–gender cell. This makes it easier to compare, say, support for universal healthcare between lower-income women and higher-income men. These are real examples of stratified sampling examples in research that show how stratification can protect against systematic bias.

Business and marketing research: stratifying customers and markets

In business and marketing, stratified sampling is used to avoid listening only to the loudest customers.

7. Customer satisfaction survey by segment

A national retailer wants to measure customer satisfaction across its entire customer base. Its internal data show three important segments:

• In-store shoppers
• Online-only shoppers
• Hybrid shoppers (both in-store and online)

If the retailer just sends a survey link to everyone and waits for volunteers, it might get mostly online shoppers responding. Instead, it builds a sampling frame and uses stratified sampling:

• All customers are classified into the three segments.
• A random sample is drawn from each segment.

Now the company can compare satisfaction scores and Net Promoter Scores across all three segments with confidence. This is a very practical example of stratified sampling in research that shows up in real corporate analytics teams.

8. Market research by region and store size

A 2024 market research study for a grocery chain might want to test a new product line in a way that represents different store environments. Researchers might define strata by:

• Region (West, Midwest, South, Northeast)
• Store size (small, medium, large)

Within each region–size combination, they randomly select stores to participate in a pilot launch and collect sales and survey data. This prevents the study from being dominated by large urban stores and gives a more accurate picture of how the product might perform nationwide.

Again, this is one of those real-world examples of stratified sampling examples in research that’s easy to adapt for a class project or a business case study.

Environmental and climate research: stratifying by geography and risk

Environmental scientists often work with highly uneven landscapes—some areas are high-risk, others low-risk. Stratified sampling helps ensure that both are studied appropriately.

9. Air quality monitoring by urban, suburban, and rural zones

Suppose researchers are studying particulate matter (PM2.5) levels across a state to compare with EPA air quality standards (epa.gov). They might:

• Divide the state into three strata: urban, suburban, rural.
• Within each stratum, randomly select monitoring sites (or neighborhoods).

Urban areas may get more sampling points because pollution is more variable, but rural areas still receive dedicated coverage. This is a clear example of stratified sampling in research where geography is the main stratification variable.

10. Climate vulnerability mapping by coastal vs. inland communities

In climate adaptation research, stratified sampling is often used to compare coastal communities at high risk of flooding with inland communities at lower risk. A 2024 study might:

• Classify all census tracts in a state as coastal high-risk, coastal moderate-risk, or inland.
• Within each risk stratum, randomly select tracts for detailed household surveys on preparedness, insurance coverage, and evacuation plans.

Because the population is unevenly distributed across these risk categories, simple random sampling might miss high-risk areas. Stratified sampling ensures that the best examples of stratified sampling examples in research in climate science capture those vulnerable communities.

Why researchers pick stratified sampling over simple random sampling

Across all these fields, the logic is similar. Researchers choose stratified sampling when:

• They know certain subgroups differ in important ways (health risk, voting behavior, purchasing patterns).
• Those subgroups might be too small to study reliably with a simple random sample.
• They want to compare groups directly (public vs. private schools, urban vs. rural residents, coastal vs. inland communities).

In many of the examples of stratified sampling examples in research above, stratification increases precision. Within each stratum, responses tend to be more similar to each other than to responses in other strata, which can reduce sampling error for a given sample size.

Researchers also use proportionate and disproportionate stratified sampling:

• In proportionate stratified sampling, each stratum’s sample size matches its share of the population.
• In disproportionate stratified sampling, some strata are intentionally oversampled (for example, rare disease patients or small ethnic groups), and then weighted later during analysis.

Election polls, health disparity studies, and studies of rare conditions are common real examples of stratified sampling examples in research using disproportionate designs.

Building your own stratified sample: a mini blueprint

If you’re designing your own study and want to mirror these examples of stratified sampling in research, a simple workflow looks like this:

1. Identify meaningful strata. Use variables that matter for your research question: age, sex, school type, region, income, risk level, etc.
2. Get population counts for each stratum. Government statistical agencies, such as the U.S. Census Bureau or NCES, are good starting points.
3. Decide whether to sample proportionately or disproportionately. If some strata are tiny but analytically important, oversampling them often makes sense.
4. Randomly select within each stratum. Use random number generators, systematic sampling, or random-digit dialing, but keep the process consistent.
5. Document everything. In your methods section, be explicit about how you defined and sampled each stratum so others can evaluate your work.

When you write up your methods, it helps to reference well-known examples of stratified sampling examples in research, such as NHANES or NCES surveys, to show that your approach follows established practice.

FAQ: common questions about stratified sampling examples

What is an example of stratified sampling in health research?

A classic example of stratified sampling in health research is a national survey that divides the population into age and sex strata, then randomly selects participants within each group to estimate rates of obesity, diabetes, or hypertension. NHANES, run by the CDC, is a widely cited real-world case.

What are some of the best examples of stratified sampling examples in research?

Some of the best examples of stratified sampling examples in research include:

• National health surveys that stratify by age, sex, and region.
• Education assessments that stratify by school type and location.
• Election polls that stratify by region, age, race, and education.
• Environmental studies that stratify by urban, suburban, and rural zones.

These designs show how stratification can improve accuracy and allow for meaningful subgroup comparisons.

Can you give examples of stratified sampling in business and marketing?

Yes. Real examples of stratified sampling in business research include customer satisfaction surveys that stratify customers by channel (in-store, online, hybrid) or loyalty tier, and market tests that stratify stores by region and size to evaluate new products.

How are strata chosen in real examples of stratified sampling?

In real studies, strata are chosen based on theory, prior evidence, and practical knowledge. Researchers look for variables that are strongly related to the outcome of interest—age in health studies, school type in education research, or region and demographics in political polling—and build strata around those variables.

Where can I find published studies that use stratified sampling?

You can find examples of stratified sampling examples in research in:

• Methodology sections of articles in journals like American Journal of Public Health or Educational Researcher.
• Technical documentation for surveys on the CDC (cdc.gov), NCES (nces.ed.gov), or NIH (nih.gov) websites.

These sources often provide detailed sampling descriptions that you can cite or adapt for your own work.