Everything You Want to Know About Cluster Sampling


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Today’s post is all about cluster sampling. It’s a strategy that comes in handy when you need to poll a big audience but you’re short on time and other resources. We’ll tell you all about the ins and outs of implementing this strategy, as well as different ways to do it, the advantages of cluster sampling, and more.


Let’s get started with the basics.

What is Cluster Sampling?

The Corporate Finance Institute offers up this great definition:

“In statistics, cluster sampling is a sampling method in which the entire population of the study is divided into externally, homogeneous but internally, heterogeneous groups called clusters. Essentially, each cluster is a mini-representation of the entire population.”

They also share this graphic from WikiCommons to help us visualize the concept:

So, once a population has been divided into smaller groups (AKA: clusters) researchers or poll creators then randomly pick from these clusters to form a sample group.

How It Works

Next, let’s break down the process of cluster sampling into steps.

1. Defining your population

The first step involves defining your population, the same way you would with other types of sampling. 

For example, if you’re surveying your customers, all of your existing customers would make up your population. 

2. Separate your sample into clusters

Now it’s time to divide your population or sample into clusters.

As for the best practices for doing so, Scribbr explains:

  • Each cluster’s population should be as diverse as possible. You want every potential characteristic of the entire population to be represented in each cluster.
  • Each cluster should have a similar distribution of characteristics as the distribution of the population as a whole.
  • Taken together, the clusters should cover the entire population.
  • There not be any overlap between clusters (i.e. the same people or units do not appear in more than one cluster).

“Ideally, each cluster should be a mini-representation of the entire population. However, in practice, clusters often do not perfectly represent the population’s characteristics, which is why this method provides less statistical certainty than simple random sampling.

Because clusters are usually naturally occurring groups, such as schools, cities, or households, they are often more homogenous than the population as a whole. You should be aware of this when performing your study, as it might affect its validity.”

Going back to the example of using your customers as your population, your clusters could include separating your sample into clusters based on the city in which they live.

3. Randomly choose clusters for your sample

It’s important to ensure this step is done randomly, or else you risk skewing the results of your study. That way, you’re effectively imitating simple random sampling.

For example, you could assign each customer a number. Then, using a randomized number generator, a number and corresponding customer can be chosen to represent that sample.

4. Collect polling data from the sample

Now it’s time for the fun part: polling your clusters to collect the data you’re looking for!

For the customer example, this could mean polling them about which product they’d most like to see next. 

Pros and Cons of Cluster Sampling

Now it’s time to start talking about the advantages and disadvantages of cluster sampling. These will help you determine when it’s an appropriate course of action for collecting the polling data you need or when a more comprehensive form of sampling might be best. 


There are several attractive benefits of cluster sampling that makes it a desirable option for researchers.

To name a few:

  • It can be done quickly which is beneficial when you’re tight on time
  • Cluster sampling is a cost-effective sampling method
  • Provided you properly cluster your population, you have a good chance of high external validity


On the other hand, there are also certain drawbacks to this type of sampling that are worth noting:

  • Even though it’s still relatively quick to pull of, it does take time to effectively plan your clusters
  • Without proper clustering, your results are less likely to accurately represent your population as a whole
  • Internal validity can be a challenge, especially compared to simple random sampling

But here’s some good news: Swift Polling makes it easy to poll a population of any size as quickly and efficiently as you can type your questions! So, that means there’s no need to limit yourself to clusters and risk the challenges that can come with this strategy.



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