Sampling methods help you structure your research more thoughtfully. However, how you group and select participants can reveal meaningful patterns or hide them from you. Read on to discover:
- What is a cluster sample, and when to use cluster sampling
- What is a stratified sample, and when to use stratified sampling
- Pros, cons, and real-world stratified vs. cluster sampling examples
- How to use Marvin’s AI-driven analysis after you’ve selected participants
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TL;DR – Stratified vs. Cluster Sampling
The way you sample participants can significantly influence the success of your user research methods.
Before we get to the nitty-gritty of stratified vs. cluster sampling, here’s a quick comparison:
| Stratified Sampling | Cluster Sampling |
| Divides users into similar groups based on key traits and randomly selects individuals from each group. | Randomly selects entire user groups (clusters) and includes everyone within those groups in the sample. |
| Pros | Pros |
| Highly representative and balanced samples Lower risk of sampling bias Clear insights from each user group | Faster and more cost-efficient Simple and easy to manage Practical for large populations |
| Cons | Cons |
| More complex, requires careful setup Higher cost and effort per sample Needs clearly defined user segments | Less precise, higher risk of group bias Less representative of diverse individuals Needs larger sample sizes to ensure accuracy |
| Best For | Best For |
| Detailed UX studies that require precise representation or accurate insights across clearly defined user segments | Projects with limited resources or when you need to gather insights quickly from naturally clustered populations (company offices, schools, neighborhoods, etc.). |
What is Stratified Sampling?
Stratified sampling is a method that probes the different layers in your user pool to select representative users.
You start by dividing your audience into smaller groups, “strata,” based on shared traits. Then, from each group, you randomly select people for your study.
It’s a structured way to make sure each group in your audience is fairly represented.
What is an Example of Stratified Sampling?
Here’s a stratified random sample example:
A streaming company like Netflix wants to understand how users discover new shows. They know their audience is diverse and that different people explore content differently.
So, they break users into clear strata based on several traits:
- Age groups (teens, young adults, adults, seniors)
- Device preference (smart TV, mobile, desktop, tablet)
- Viewing habits (binging daily, casual weekly viewers, occasional users)
- Location (North America, Europe, Asia, South America)
By monitoring random samples from these strata, the company gets solid data on how each group browses, searches, and selects content. That helps the company build smarter, tailored interfaces and recommendations, improving everyone’s streaming experience.
Is Stratified Sampling Random?
The stratified sampling method involves some randomness, but it also has structure.
While you use clear criteria to define your strata, the actual selection of participants within each stratum is random.
When to Use Stratified Sampling
When your audience naturally splits into clear groups, it makes sense to use stratified sampling. Such groups can be age ranges, device types, or user roles.
This method is all the more useful when you want detailed insights from each subgroup.
How to Do Stratified Sampling
Putting this sampling method into practice is straightforward:
- Identify key traits, such as user roles or demographics.
- Separate your users into these strata (groups) based on their common traits.
- Randomly pick users from each group, either proportionally (reflecting actual group sizes) or equally, depending on your goal.
- Combine your picks from each group into your final sample set.
What is Cluster Sampling?
Cluster sampling is a method where you randomly select entire groups, “clusters,” from your audience.
Instead of choosing individual users from every possible segment, you pick whole clusters at random. Once you select these clusters, you include everyone inside each group in your study.
What is an Example of Cluster Sampling?
Let’s look at a cluster sampling method example:
You want to design a meal-planning app. This app should serve companies in many office buildings nationwide. However, visiting each office to interview individual users would cost too much time and money.
Instead, you randomly pick several office buildings — one in Boston, one in Chicago, and one in San Francisco. You then include all the employees from each chosen building in your research.
Because each office building naturally represents typical users, you get actionable insights fast and at a lower cost. These insights help your team design better user experiences for busy professionals.
Is Cluster Sampling Random?
With cluster sampling, you randomly choose clusters (groups), not individual users. However, there is no random picking within the clusters, as in stratified sampling.
Once a cluster is selected, everyone in that group becomes part of your sample automatically.
When to Use Cluster Sampling
This method works best when users naturally form similar groups or when individual sampling is too costly or complicated. It’s also great when users are grouped by clear units such as offices, neighborhoods, schools, or entire cities.
One of the biggest advantages of cluster sampling is that it allows you to save resources. If you need quick insights from representative groups within a large audience, this is your go-to approach.
How to Do Cluster Sampling
Here’s how you do cluster sampling step-by-step:
- Identify natural groups or clusters in your user population (such as physical locations or digital environments).
- Randomly select a few of these clusters (user communities, Slack channels, etc.).
- Include all members of the selected clusters in your study.
- Analyze the data, knowing your results reflect the full user experience within chosen clusters.
Tip: With cluster sampling, you often end up handling larger datasets. Effectively capturing and analyzing insights is crucial, which is why a tool like Marvin can help you tremendously.
Book a free demo today and see how you can use Marvin to manage large sets of interviews and surveys!
Relevant Characteristics Between Stratified and Cluster Sampling
Want to easily choose between the two methods on your next UX research project?
Take a look at the following side-by-side table that shows how their relevant characteristics stack up:
| Stratified Sampling | Cluster Sampling | |
| Purpose | Ensure all user segments are fairly represented | Quickly sample entire user groups at once |
| Grouping Basis | Shared traits such as age, behavior, or device type | Natural groupings such as location or organization |
| Homogeneity Within Groups | High Users within strata are very similar | Low Users within clusters often vary widely |
| Heterogeneity Within Groups | Low Groups are intentionally similar internally | High Clusters often include diverse users |
| Sampling Method | Random selection of individuals within defined strata | Random selection of entire groups (clusters) |
| Efficiency | Moderate Requires careful setup of strata | High Quickly collects data from entire clusters |
| Representativeness | High Carefully balanced across user type | Moderate Can miss nuances if clusters differ strongly |
| Complexity | Higher Requires more upfront planning and detailed definitions | Lower Becomes simpler to manage after clusters are chosen |
| Use Case Scenarios | Detailed studies that require insights across all user types | Fast studies across groups, such as offices or cities |
| Cost Considerations | Higher Requires more resources to sample individuals | Lower Cost-effective when travel or resources are limited |
Similarities and Differences
So far, we’ve put these two sampling methods side by side so you can understand them at a glance.
Next, we’ll dive a little deeper into what sets them apart and what ties them together:
Stratified and Cluster Sampling Differences
The biggest difference between stratified and cluster sampling is how you pick participants.
- With stratified sampling, you divide users into groups based on key traits (age, device type, etc.). Then, you randomly pick individuals from each group to ensure a balanced representation of all user types.
- Cluster sampling flips this around. You randomly select entire groups (offices, schools, etc.) and include everyone inside each selected cluster.
Another key difference is group homogeneity.
- Stratified groups (strata) are internally similar but differ greatly from each other.
- In contrast, clusters usually contain diverse users within each group, even though clusters themselves might be similar overall.
Finally, they differ in efficiency and cost.
- Cluster sampling is typically faster and cheaper since you sample full groups simultaneously.
- Stratified sampling takes more planning and resources because you select individual users carefully within each group.
Stratified and Cluster Sampling Similarities
Both methods require that you divide your user base into clear groups as a first step. In both cases, you’re grouping users based on something meaningful for your research.
Another similarity is that both methods use randomness, ensuring fairness and reducing sampling bias. Stratified sampling randomly picks individuals within each stratum, while cluster sampling randomly chooses entire clusters.
Lastly, both approaches help UX researchers manage resources efficiently. Instead of sampling everyone, you strategically narrow down user groups. This saves valuable time and budget while providing trustworthy insights for better product design decisions.
What About Systematic Random Sampling?
Systematic random sampling means picking users at regular intervals from your user list. You first pick a random starting point, then select every nth user afterward. It’s simple, straightforward, and ensures randomness without complicated grouping.
For example, you’re testing a checkout process with 1,000 recent customers.
You randomly start with customer #7. Then you pick every 10th user afterward: #17, #27, #37, and so on. This gives you a clean sample without bias and helps you quickly gather data on the checkout experience.
Unlike stratified sampling, systematic sampling doesn’t break your users into specific groups. You’re not targeting representation from different user segments.
And unlike cluster sampling, you’re not picking entire groups of users, just evenly spaced individuals from a single list.
Systematic random sampling is ideal when:
- Users don’t naturally form distinct groups
- Simplicity and speed matter
It’s a fast way to create unbiased samples for usability tests or quick user surveys without a complicated setup.
Frequently Asked Questions (FAQs)
These FAQs on stratified vs. cluster sampling should give you more context:
When Should You Use Stratified Sampling Instead of Cluster Sampling?
Unlike cluster sampling, which is quicker and cheaper, stratified sampling is more resource-intensive but also more precise.
Use stratified sampling when your audience clearly splits into meaningful groups, such as user roles or devices. Also, consider it when you need better user representation and more balanced insights.
How is the Population Divided in Stratified Sampling?
In stratified sampling, the population is divided into groups called “strata.”
Each stratum contains users who share important characteristics, such as age, device type, or user role. Members within each stratum are similar to each other but clearly differ from those in other strata.
How is the Population Divided in Cluster Sampling?
In cluster sampling, the population is divided into natural groups called “clusters,” such as office buildings, neighborhoods, or schools.
Each cluster typically includes users with diverse traits. Although clusters resemble each other overall, individual users within a cluster may vary significantly.
Can Stratified and Cluster Sampling Be Used Together?
Yes, combining methods is called stratified cluster sampling. First, you divide users into strata based on key traits. Then, you randomly pick entire clusters within each stratum.
It gives a balanced representation (stratified benefit) while saving time by sampling groups (cluster benefit). This approach facilitates more detailed but cost-effective research.
Bottom Line
Both methods – stratified and cluster sampling – help UX researchers gather reliable insights, but each shines under different conditions:
- Stratified sampling gives you a precise, balanced representation of carefully defined user groups.
- Cluster sampling offers speed, efficiency, and lower costs when your audience naturally forms clear clusters.
Whichever method fits your next research project, your sampled audience is just the first step. For your chosen sample, you’ll need to capture, analyze, and share the insights you uncover. Let Marvin be the research repository where you do all that.
Our AI-powered research assistant supports automated transcripts, precise time-stamped notes, effortless tagging, and AI-enhanced insights. It lets you quickly turn sampled user conversations into actionable findings.
Create your free Marvin account now and start turning user conversation samples into actionable findings.
