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Cluster Random Samples: Definition, Selection & Examples

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  • 0:01 Cluster Random Sampling
  • 0:28 What Is Cluster Sampling?
  • 1:20 How to Use Cluster Sampling
  • 3:33 When to Use Cluster Sampling
  • 5:09 Lesson Summary
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Lesson Transcript
Instructor: Cathryn Jackson

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling.

Cluster Random Sampling

Donna is running for student class president. She has some key issues in her campaign, such as bullying in the school, the prom theme, and fixing the water fountains in the school. Her campaign manager, Lulu, wants to collect information about how the students feel about the different issues in the campaign. She specifically wants to know about the different interest groups in the school.

In this lesson, you will learn about cluster sampling, including what it is, how to use it, and some of the advantages and disadvantages of using this sampling method.

What Is 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.

For example, Lulu wants to conduct some marketing research for Donna's campaign. She specifically wants information from the different interest groups of the school. In the school, she has found that 30% of the students are involved in athletics, 25% of the students are involved in an academic club, 20% of the students are involved in an art or theater club, and 25% are involved in a music club. None of the students are involved in more than one club and all of the students are involved in a club. Lulu knows that Donna is a member of an athletics club and the athletics students support Donna's campaign.

How to Use Cluster Sampling

When conducting research and using cluster sampling, you must keep a few things in mind:

  1. Cluster random samples cannot have crossover.
  2. Cluster random samples must include all members of a population.

Let's start with the rule that cluster random samples cannot have crossover. In other words, each of the clusters must be mutually exclusive. In this case, Lulu cannot divide the school population into interest groups where students are involved in more than one club or are a member of more than one cluster. Lulu has decided to conduct research with only the students that are in the arts, theater, and music clubs. Since Lulu already knows that no students are involved in more than one of each of the club categories, she knows her cluster sample does not have crossover.

We also know that cluster samples must include all members of a population, meaning that all of the students in the school must be a member of one of the four clubs. If there are students that are not a member of one of the clubs, then the cluster sample does not work.

If Lulu doesn't have time to give a survey to all the students in the club, she can use two-way cluster sampling, which is a sampling method that involves separating the population into clusters, then selecting random samples from each of the clusters. Lulu can use simple random sampling to select members of each cluster, or club, to give a survey.

You may be wondering how two-way cluster sampling is different from stratified sampling.

  1. Stratified random samples must have an equal selection from each group that is proportionate to the population. Cluster sampling selection does not have to be equal; however, the clusters should be as close to the same size as possible.
  2. Stratified random samples should not divide the population into more than six groups and are usually organized by demographic. Cluster sampling can be many groups and can be based on anything, including interests, hobbies, political views, geographical location, etc.

Because Lulu is using cluster sampling, she can select as many or as few people from each group as she wants.

When to Use Cluster Sampling

You should use cluster sampling when:

  • The entire population is unclear or unknown
  • The sample clusters are geographically convenient
  • The clusters are 'natural' in a population

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