Cluster Sampling - Definition & Examples

When we talk about “stages” in the context of sampling, it indicates the number of steps taken toward selecting the desired sample group. 

Let’s go over the three main types of this sampling method:

1. One-Stage Sampling

One-stage sampling, also known as single-stage cluster sampling, is a method where every element within the selected clusters will become a part of the sample group. This is oftentimes not feasible if the target population is vast, and the clusters are too large to include fully.

For example, if you were to conduct a study on the consumption of soda in a particular city, you could use area sampling to divide the city into different areas, called clusters, and then select certain clusters to be a part of the sample group. 

2. Two-Stage Sampling

Two-stage sampling is a more feasible and realistic method of sampling in cases where the population is too large or is scattered over a large geographical area. 

In this method, simple random sampling (sometimes other sampling methods like systematic sampling are also used) is used to select elements from the selected clusters, further narrowing down to the desired sample size.

Carrying forward the previous example, if your sample is too large even after eliminating the clusters that weren’t selected, you may use two-stage sampling to further narrow down the sample. 

With two-stage sampling, you can use simple random sampling to select elements from each one of the selected clusters. The units of the narrowed-down sample group will be the selected respondents for the study on soda consumption.

3. Multistage Sampling

Multistage sampling takes two-stage sampling further by adding a step, or a few more steps, to the process of obtaining the desired sample group. This means that you need to use multiple steps to obtain the desired sample, and at each stage, you are left with a smaller and smaller sample group. 

This is the most complex of the three cluster sampling methods but is also the most advantageous for very large populations and/or geographically dispersed populations.

To further build on the example of the study of soda consumption, let’s assume the city you are researching is a highly populous one like New York. In such a case, it’s probable that even after implementing two-stage sampling, you may not reach your desired sample size. You can then take further steps to obtain your desired sample size using multistage sampling.

These are three ways this sampling method is categorized. Now let’s look at the steps to cluster sample.