What is Cluster Sampling?

May 5, 2018 Author: munishmishra04_3od47tgp
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Cluster sampling (also known as one-stage cluster sampling) is a technique in which clusters of participants that represent the population are identified and included in the sample. Cluster sampling involves identification of cluster of participants representing the population and their inclusion in the sample group. This is a popular method in conducting marketing researches.

The main aim of cluster sampling can be specified as cost reduction and increasing the levels of efficiency of sampling. This specific technique can also be applied in integration with multi-stage sampling.

Overview of Cluster Sampling

Cluster sampling is the sampling method where different groups within a population are used as a sample. This is different from stratified sampling in that you will use the entire group, or cluster, as a sample rather than a randomly selected member of all groups.

It’s a sampling method used when assorted groupings are naturally exhibited in a population, making random sampling from those groups possible. The use of the technique requires the division or classification of the population into groups, defined by their assorted characteristics or qualities. These groups are then called clusters.

Cluster sampling is commonly used for market research because of its ability to help account for the common interest of a larger population at a relatively lower cost. Larger companies may find interviewing all their customers as nearly impossible, but the classification of their customers into clusters will help them to randomly sample some clusters as primary data for the interview.

One very important factor to consider when using this sampling technique is that an element in the population can only be classified or assigned to a single cluster.

Ultimately, in cluster sampling

  • Divide the whole population into clusters according to some well-defined rule.
  • Treat the clusters as sampling units.
  • Choose a sample of clusters according to some procedure.
  • Carry out a complete enumeration of the selected clusters, i.e., collect information on all the sampling units available in selected clusters.

Types of Cluster Sample

One-Stage Cluster Sample

Recall the example given above; one-stage cluster sample occurs when the researcher includes all the high school students from all the randomly selected clusters as sample.

Two-Stage Cluster Sample

From the same example above, two-stage cluster sample is obtained when the researcher only selects a number of students from each cluster by using simple or systematic random sampling.

Advantages and Disadvantages of Cluster sampling


  • Generating sampling frame for clusters is economical, and sampling frame is often readily available at cluster level
  • Most economical form of sampling
  • Larger sample for a similar fixed cost
  • Less time for listing and implementation
  • Also suitable for survey of institutions


  • May not reflect the diversity of the community.
  • Other elements in the same cluster may share similar characteristics.
  • Provides less information per observation than an SRS of the same size (redundant information: similar information from the others in the cluster).
  • Standard errors of the estimates are high, compared to other sampling designs with same sample size

Example of Cluster Sampling

Cluster sampling is often confused with stratified sampling, because they both involve “groups”. In reality, they’re very different. In stratified sampling, we split the population up into groups (strata) based on some characteristic.

A cluster sample is obtained by selecting all individuals within a randomly selected collection or group of individuals.

In essence, we use cluster sampling when our population is already broken up into groups (clusters), and each cluster represents the population. That way, we just select a certain number of clusters.

With our visual, let’s suppose the 12 individuals are paired up just as they were sitting in the original population.

Cluster Population

Figure: Cluster Population 1

Since we want a random sample of size four, we just select two of the clusters. We would number the clusters 1-6 and use technology to randomly select two random numbers. It might look something like this:

Cluster Population

Figure: Cluster Population  2


[1] “Cluster Sampling”, available online at: https://research-methodology.net/sampling-in-primary-data-collection/cluster-sampling/

[2] Saifuddin Ahmed, “Cluster Sampling”, Dept. of Biostatistics School of Hygiene and Public Health Johns Hopkins University

[3] “Cluster Sampling”, available online at: https://explorable.com/cluster-sampling

[4] “What is Cluster Sampling?” available online at: https://www.myaccountingcourse.com/accounting-dictionary/cluster-sampling

[5] “Section 1.4: Other Effective Sampling Methods”, available online at: https://faculty.elgin.edu/dkernler/statistics/ch01/1-4.html

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