GuidesResearch methodsStratified vs. cluster sampling

Stratified vs. cluster sampling

Last updated

14 March 2023

Author

Dovetail Editorial Team

Reviewed by

Cathy Heath

In different types of studies, sampling refers to how researchers select members of the population to be in the study.

Cluster and stratified sampling methods offer different ways for researchers to increase their accuracy. 

Understanding each method and their differences can help you make the right choice when preparing for a study. Let’s dive in.

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What is stratified sampling?

Stratified sampling is a method to divide a target population into specific groups. You randomly select members from those groups to participate in the study. 

Stratified sampling can help researchers create homogeneous groups that meet specific criteria.

Examples of stratified sampling

A new product

When identifying a target audience for a new product or business, it's crucial to determine who will be most interested in the new offering. 

You can identify which subgroups will be ready for market research by dividing the target population into groups based on age, gender, or other criteria.

In the workplace

For employers to gauge employee satisfaction in all areas, it's essential to understand the difference across varied levels of seniority. 

To achieve this, the researcher can divide employees into groups based on their job descriptions, revealing whether executives are more satisfied than lower-level employees. 

Is gender a stratified sample?

Yes. Researchers use stratified sampling to divide populations into groups based on specific characteristics, such as gender, age, income, or education level. 

For example, with studies that require an equal sampling of genders for accuracy, the researcher may divide the target population into gender groups and randomly select members.

What is cluster sampling?

Cluster sampling is a method where a geographic or spatial characteristic naturally divides the target population into random clusters or groups, such as neighborhoods, schools, or hospitals.

The researcher randomly chooses entire clusters to take part in a study. The selection doesn't require participants to meet predetermined requirements. This approach is often appropriate when it’s impractical or too expensive to sample people individually.

Examples of cluster sampling

One-stage cluster

A local donut shop wants to determine how many people prefer their products over a large chain. The shop splits the town into several neighborhoods and randomly selects people to form cluster samples. 

Since there are no prerequisites for the sample group, every member chosen from the neighborhood can participate in the research. 

Two-stage cluster

The chain coffee shop wants to know how all its branches are performing. The owner clusters branches based on their location and randomly selects samples from the cluster for studying the performance.

What is random vs. cluster sampling?

Random sampling takes a small random portion of the entire population to represent the whole data set. In random sampling, every member has an equal probability of being chosen. 

While stratified sampling takes random numbers of select group members, separating the target population into groups ensures equal representation of the population.

When to use each sampling method

Cluster and stratified sampling are effective sampling methods for conducting a study. They both divide a population into groups. 

However, if a population has natural differences, then stratified sampling is best. 

What are the advantages of stratified sampling?

  • Randomly selected members from equally divided subgroups

  • Improved precision in comparison to non-homogeneous sampling

  • Reduced errors

What are the advantages of cluster sampling?

  • Cost reduction

  • Randomly selected clusters

  • Naturally formed divisions

Key differences between stratified and cluster sampling

While both sampling methods depend on dividing a population into subgroups, the process of choosing members yields different results.

Cluster sampling begins by dividing a population into groups that often have a shared geographical location before choosing all members of random groups.

Stratified sampling divides a population into specific groups relating to an interest and includes some members of all the groups.

These are the critical differences between stratified and cluster sampling:

  • You divide groups in stratified sampling by specific traits while randomly selecting groups in cluster sampling from existing groups.

  • Stratified sampling gives the researcher more control by allowing them to choose particular groups.

  • There’s homogeneity between groups in cluster sampling but within groups in stratified sampling. 

  • Stratified sampling includes an equal representation of the diverse group, while cluster sampling uses members from the entire group.

  • Stratified sampling aims to improve precision and representation, while cluster sampling aims to improve cost-effectiveness and operational efficiency. 

Which sampling method is best, and why?

The best sampling method depends on your needs, the available target population, and the study’s parameters. 

Stratified sampling is preferable when the differences in the individual groups will affect the study’s outcome. 

Cluster sampling is best when the diversity within a cluster shouldn't vary from each other. 

Which sampling is more accurate?

Stratified sampling is typically more accurate because the researcher chooses specific categories that align with the needs of the study. The researcher can ensure equal representation by dividing the target population into specific groups before random selection.

Which sampling is more efficient?

Cluster sampling improves cost-effectiveness and operational efficiency by selecting entire clusters that are already naturally divided. 

Those within the target population automatically fit the study criteria, making every member eligible to participate. 

How to choose between stratified and cluster sampling

Stratified and cluster sampling have many similarities, but their differences usually mean one type of sampling is more effective for a specific study. 

These tips can help you choose the sampling strategy that aligns with the goals of your study:

  • Determine your experiment’s goals to decide which sampling type best matches your study objectives.

  • Consider what type of data is relevant to the experiment.

  • Choose an effective method of gathering data. If precision is key, stratified sampling is likely more effective.

  • Consider variables such as time constraints and budget. If cost-effectiveness and efficiency are critical, cluster sampling is more likely to meet your needs. 

Matching your research style to your project needs

How you conduct research contributes to the outcome of your study. Learning more about different sampling methods can help you become more accustomed to making choices that align with each experiment.

Stratified and cluster sampling are two main ways researchers choose study subjects for investigations. Selecting the best method can help you conduct an accurate study with results most likely to fit your needs.

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