Stratified random sampling: definition, examples, and guide
Stratification is one of the best methods researchers and data analysts can use when they need to accurately and meaningfully collect data.
Unlike simple random sampling (which considers the population as a single group), stratified random sampling divides the population into small subgroups (strata) to ensure that all essential segments are represented.
The stratification sampling method can save time and prevent errors when you conduct market analysis, study public health, research a country's population, and more, resulting in more accurate conclusions.
In this article, we'll explain what stratified random sampling is, how to use it, outline the benefits of the method, and give you some examples of stratified sampling in the real world. Want to collect data more accurately? Keep reading!
What is stratified random sampling?
Stratified random sampling is a technique that divides a population into small, distinct groups, which are referred to as strata. Every stratum may have a common characteristic (age, income level, gender, geographic location, or another demographic data point). After dividing the population, researchers randomly select samples from each stratum.
With stratified random sampling, researchers can be sure that every subgroup is properly represented and the possibility of missing major patterns is minimized.
Sometimes you will see the terms "stratified sampling" and "stratified random sampling" used interchangeably. Yes, both generally refer to the same process, though "stratified random sampling" emphasizes that the selection within each stratum is random, which is a crucial factor for reducing bias.
How to use stratified random sampling in research
Stratified random sampling highlights the diversity of the population surveyed. Researchers, insights specialists, and data analysts divide the population into strata based on different characteristics (location, age, income, and so on) so they can capture differences between groups and avoid over- or under-representing certain segments.
An example of stratified sampling: A large enterprise's goal is to understand employee satisfaction. The company uses its enterprise survey software platform and divides employees into strata based on location (HQ/regional offices) and job level (senior/mid/junior).
Within each of these strata, specialists randomly select a set number of employees to complete the survey. This ensures that voices from each location and level are properly represented. The company then compares results across strata (senior vs. junior in HQ, regional staff vs. HQ staff) and uses the findings to tailor engagement strategies for each subgroup.
Without the proper stratification in this stratified random sampling example, some groups might be overlooked.
Using a stratified random sampling method improves research in several ways:
- Enhanced accuracy: By sampling from each subgroup, the researcher can make more precise estimates about the population.
- Better comparisons: Researchers and data analysts are able to compare the results between strata, like age groups, income levels, or regions.
- Greater efficiency in the use of resources: The researchers focus on smaller groups instead of having to survey the whole population.
- Reduced bias: Random selections in every stratum eliminate unfair weighting of any single subgroup, making conclusions more valid.
Stratified random sampling is useful in many fields. It helps in market research to understand the customers' preferences across demographics, allows tracking of disease tendencies across age or risk groups, and ensures that policies in government surveys are informed by data representing all communities.
This approach works great for researchers who want to make evidence-based decisions by capturing a complete and accurate view of the population they are studying.
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The guide to using stratified random sampling
Stratified random sampling may appear complicated, but when done in clearly divided steps, the process is simple.
Here's how researchers can do it:
- To define the population. These specialists identify the whole group that they want to research. For example, all customers of a company or all local residents in the city.
- To identify strata. This involves segmenting the population into meaningful subgroups based on the characteristics that are of interest to you for your research, such as age, gender, income, education level, or region.
- To determine the right stratified sample size. Experts decide how many individuals are to be selected from each subgroup. This may be proportional, i.e., dependent on the size of each stratum within the population, or equal when comparison is the priority.
- To use random sampling within strata. They use random methods (such as random number generators) when selecting participants in each subgroup to ensure fairness and reduce bias.
- To collect data. They survey or interview the chosen subjects, or observe them, as they normally would in a research study.
- To analyze the results. Data analysts and researchers combine data from all strata and compare them to obtain a complete picture of the population. We also recommend analyzing each stratum separately to see differences or patterns.
By following the steps in this researcher example, you can ensure that your stratified sample accurately represents the population, making your results reliable and meaningful.
The benefits and drawbacks of using stratified random sampling
Stratified random sampling certainly has its advantages, but it also has some disadvantages. Understanding both will help you decide when and how to use this popular technique.
In brief, stratified random sampling is more effective in studies when population subgroups are relevant. It yields better representation and precision than simple random sampling but needs thoughtful planning and an understanding of the population.
Examples of stratified random sampling
Stratified random sampling is used in different fields to ensure that all important subgroups are captured in a study. Here's how it's used in government, healthcare, market research, and education.
Government surveys
Let's imagine researchers want to conduct a national census to study employment rates in urban and rural areas. The population is divided into urban and rural strata, and random samples are selected from each to ensure both areas are fairly represented.
One stratified sampling example is the Future of Families Study. It included children born in large US cities between 1998 and 2000. Hospital- and city-level strata were used for selection, including oversampling of families with unregistered parents.
Market research
A company wants to understand customer satisfaction – how customers feel about their products at various age groups. They stratify their customers by age group, such as 18–25, 26–40, and 41–60. Then, the specialists randomly survey customers from each age range to ensure that all age groups provide their views, and a conclusion is drawn.
Healthcare research
Here's a real-life situation: a hospital researches the effectiveness of a new treatment across patients of different risk levels. Patients are divided into low, medium, and high-risk strata from which random samples are selected to test the outcomes, with greater accuracy assured.
Education studies
Researchers want to compare test scores between public and private schools. Schools are divided into either the public or private strata. Students are randomly selected from both strata to get results reflecting actual differences among schools of each type.
National Longitudinal Study (1972) is a great stratified sampling example of an educational study. In 1972, high school students were selected using a two-stage stratification method: first, schools by region, type, and composition; second, students within schools.
These stratified sampling examples show how stratified random sampling methods help researchers gain balanced, representative, and reliable data across different fields and populations.
Avoiding bias in stratified random sampling and best practices
Stratified random sampling is designed to reduce bias, but it can still happen if the process is not carefully carried out. Best practices ensure that the obtained results are valid and dependable.
Common sources of bias
Many of the main issues with bias stem from mistakes in defining the strata themselves or in how participants are selected.
- Incorrect definition of strata. Lack of meaningful subgroups or overlap of subgroups means some sections are underrepresented.
- Non-random selection within strata. Selecting participants without true randomness can lead to biased results.
- Unequal response rates. If some strata respond less than others, then the stratified random sample may no longer represent the population.
Best practices to avoid bias
Even with stratified random sampling, bias can occur if the methodology is not carefully followed. These best practices can help to eliminate bias:
- Carefully define strata: The characteristics used might be relevant to your research, such as age, income, location, or risk level.
- Use true random selection within each stratum to select participants using random methods, such as random number generators.
- Ensure that the stratified random sample size for each stratum is proportional to its share of the total, unless there are valid reasons to deliberately over-sample some.
- Monitor response rates: Encourage participation across all strata and adjust for low response rates if necessary.
- Document your process: Keep clear records of how strata were defined and how samples were selected to maintain transparency.
By applying these best practices, you'll maximize accuracy, reduce bias, and produce reliable, representative results by carefully defining the strata and maintaining randomness.
How to use disproportionate stratified random sampling
In some instances, stratified random sampling might need to be tweaked to be the best method to get an accurate representation of all the subgroups within a population.
For example, if you use a simple random sample and a subgroup within the population is a small percentage, you may not get enough respondents from that subgroup to draw accurate conclusions, as discussed in the paper, "Survey design and analysis considerations when utilizing misclassified sampling strata".
In these instances, you can employ disproportionate stratified sampling in order to ensure you get enough data on each subgroup.
Final thoughts
Stratified random sampling is a powerful tool for researchers wanting accurate, reliable, and representative data. It's used in business, government, and academic fields to ensure that research results are representative of all key subgroups of a population.
Utilized in market research, this method helps portray the varied tastes and behaviors of customer segments to help businesses make better marketing and product decisions.
In government, especially when using government survey solutions, stratified random sampling ensures that surveys and statistics are representative of the whole population to ensure more effective policy formulation.
In academic research, stratified random sampling enhances the reliability of findings and allows meaningful comparisons between important groups.
Software tools like Checkbox help researchers design stratified surveys that capture insights from every subgroup. Custom questions and advanced analytics let you collect accurate data across different strata (such as age, location, or job level), and transform these insights into data-backed decisions.
Want to create surveys that truly represent your audience. Request a demo!
Stratified random sampling FAQs
This method ensures that all key subgroups of the population are represented. It improves accuracy and allows meaningful comparisons between different strata for researchers and data analysts.
Stratified sampling is designed to reduce bias. However, that bias can still occur if strata are defined poorly or when selection within strata is not truly random.
Yes. Participants within each stratum are randomly chosen to minimize partiality and research bias.
Stratified sampling definition refers to a technique of drawing samples by dividing the population based on common characteristics into smaller subgroups called strata, from which random samples are drawn.
