Cluster Sampling: Definition, Advantages & Examples

What is Cluster Sampling?

Cluster sampling is a method of obtaining a representative sample from a population that researchers have divided into groups. An individual cluster is a subgroup that mirrors the diversity of the whole population while the set of clusters are similar to each other. Typically, researchers use this approach when studying large, geographically dispersed populations because it is a cost-controlling measure. This technique is a probability sampling method.

Researchers do not need to obtain samples from all clusters because each one reflects the entire population, and their homogeneity makes them interchangeable, which simplifies the sampling process. These groups should be mutually exclusive—people can’t be a member of more than one. Collectively, the groups should contain all members of the population you’re studying. Usually, researchers use existing groups as the clusters, such as cities, schools, and business sites.

Geographic groupings are the most common type. The rationale for using them is that it is impractical to obtain samples from wide-ranging geographic regions. Cluster sampling reduces the geographic areas from which you recruit subjects yet can still produce representative samples. Learn more about representative samples.

For more information about using samples to draw conclusions about populations, read my articles about Populations, Parameters, and Samples in Inferential Statistics and Descriptive versus Inferential Statistics.

Learn about Types of Sampling Methods in Research.

Cluster Sampling Example

For example, imagine we are studying rural communities in a state. Simple random sampling requires us to travel to all these communities just to get a few subjects from each place, which could be cost and time prohibitive. However, we can divide rural communities into similar groups. Then, we pick a random sample of communities and focus our efforts on them.

We don’t need to travel to all geographic regions, only a randomly selected subset.

Benefits of Cluster Sampling

Many surveys and studies use this method because it provides crucial benefits.

Increases Sampling Feasibility

In simple random sampling, researchers need to create a list containing all subjects in the population. That task can be difficult or impossible when you’re studying a large population spread out over a broad geographic region.

However, researchers using cluster sampling only need to devise a list of subjects for the groups they use in the study. It increases the practicality of sampling from a large population.

When creating a sampling frame for an entire population is impossible, cluster sampling might be the only feasible method for obtaining a representative sample.

If you don’t have any population list at all, consider using systematic sampling. Convenience sampling also does not require a list but the results are minimally useful.

Reduces Travel and Administrative Costs

Administering a study that covers an extensive geographic area can be cost prohibitive. The project can significantly reduce travel and administrative costs by using cluster sampling to decrease the geographic scope to fewer locations.

Larger Samples

By using cluster sampling, researchers can collect larger samples than other methods because the groups simplify and reduce data collection costs. Clustering effectively concentrates the subjects into smaller regions, allowing the researchers to sample more of them. For example, if they use schools as their groups, instead of randomly selecting students from scattered schools, they can use all students from the schools they randomly select.

Disadvantages of Cluster Sampling

Design Complexity

Cluster sampling can increase the complexity of the design. Investigators need to pay attention to how well the groups approximate the overall population and how homogeneous they are to each other. Both factors can affect their sampling plan. Analyzing the data is also more complex because they’ll need to weight the subjects appropriately to calculate the estimates and confidence intervals.

Accuracy and Validity Issues

Cluster sampling might not entirely represent the population. Ideally, the groups mirror the full diversity of the entire population. Realistically, that’s often not the case. Frequently, they are small, naturally occurring groupings that tend to be a bit more homogeneous than the whole population.

Consequently, cluster samples tend to contain more sampling error than simple random sampling, producing less accurate estimates. On the other hand, you can often draw larger samples using this method, potentially offsetting the sampling error.

Finally, because cluster sampling might not be fully representative, it can affect the ability of your study to draw valid conclusions about the population.

Related post: Sample Statistics are Always Wrong (to Some Extent)!

Single-Stage vs. Two-Stage Cluster Sampling

After researchers identify their clusters, they need to decide which approach they’ll use, single-stage or two-stage sampling.

Single-Stage

Single-stage sampling recruits all subjects from each group that the researchers select.

Follow these steps for single-stage cluster sampling:

1. Identify the clusters.
2. Randomly select a portion of them.
3. Use all subjects within the selected clusters.

Use single-stage sampling when each cluster fully represents the population’s diversity and they are homogeneous as a group.

In this scenario, single-stage cluster sampling produces unbiased estimates because all groups are fully representative and interchangeable. However, when conditions are sufficiently different from the ideal case, the researchers need to consider using two-stage cluster sampling.

Two-Stage

Two-stage sampling recruits a random sample of subjects from each group that the researchers select.

Follow these steps for two-stage cluster sampling:

1. Identify the clusters.
2. Randomly select a portion of them.
3. Randomly sample subjects from the selected clusters.

Because the researchers draw a random sample from each group rather than the entire set, they’ll obtain a smaller sample than a single-stage design. Alternatively, they can increase the number of clusters to increase their sample size.

Use two-stage sampling when the clusters do not fully represent the population or they are not homogeneous as a group. When either condition is true, the groups are not fully representative or interchangeable. Randomly sampling the subjects from the groups helps reduce the bias that these conditions cause. However, it increases the time and cost associated with the sampling plan relative to a single-stage version.

Examples

Suppose we’re studying school students and are using schools for clusters.

In a single-stage plan, the researchers randomly select the schools and then recruit all students in those schools.

In a two-stage plan, the researchers still randomly select the schools. However, within those schools, they randomly select a sample of students instead of using all the students.

Cluster Sampling vs. Stratified Sampling

Both cluster and stratified sampling have the researchers divide the population into subgroups, and both are probability sampling methods that aim to obtain a representative sample. However, beyond those similarities, the goals and techniques are strikingly different. The table highlights the differences between the two sampling methods.

Cluster Sampling	Stratified Sampling
Groups reduce costs and allow researchers to sample large populations.	Groups ensure the sample reflects all relevant subgroups and can produce better group estimates.
Each group reflects the full diversity of the population.	Each group is relatively homogeneous compared to the whole population.
Groups should be identical to each other.	Groups should be different from each other.

For more information, read my post about Stratified Sampling.

Reference

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)