Cluster Sampling - Definition & Examples

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Table of Contents

What is Sampling?

Researchers conduct online surveys to understand opinions that are relevant to their target audience (the population that interacts with the product or offerings). But not everyone is a part of the target audience, and therefore, to improve the quality of the insights generated from the survey, it is important for the researchers to understand who to include in the survey research.

Before conducting surveys using online survey software, researchers can use market research tools that have survey panel managers to create samples to ensure high survey response rates.

What is Cluster Sampling?

Cluster sampling is a type of probability sampling. This means that cluster sampling, when used, gives every unit/person in the population an equal and known chance of being selected in the sample group.

For this method of sampling, researchers divide the population into internally heterogeneous and externally homogeneous subpopulations known as clusters. The clusters are externally homogeneous as they appear to be grouped together by a shared characteristic/criteria but are internally heterogeneous because the subpopulations within the clusters have different compositions.

Clusters may be divided by different cities in a country, different areas in a city, different organizations, different universities, different industrial estates, etc. After these clusters have been decided, researchers select certain clusters and eliminate the rest. 

For example, if you’re conducting a study across all cities in the United States, you can use cluster sampling to eliminate certain cities, or clusters, in order to select your final sample group.

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Types of Cluster Sampling

Typically, there are three types of cluster sampling:

  1. One-Stage Sampling
  2. Two-Stage Sampling
  3. Multistage Sampling
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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 cluster sampling:

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. 

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.

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 the researchers use multiple steps to obtain the desired sample, and at each stage, they are left with a smaller and smaller sample group. 

This is the most complex of the three 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.

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Steps to conduct Cluster Sampling

These are the following steps used to perform single-stage cluster sampling:

Step1: Decide on a target population and desired sample size.

Step-2: Divide the target population into clusters based on specific criteria.

Step-3: Select clusters using methods of random selection while keeping in mind the desired sample size.

Step-4: Collect data from the final sample group.

Further steps may be taken using two-stage or multistage sampling to achieve desired sample size if it cannot be achieved through one-stage sampling.

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What are the advantages of Cluster Sampling?

There are quite a few advantages to using cluster sampling such as

1. Easy to implement

Cluster sampling is relatively easy to implement. 

2. Very efficient

This method of sampling is more cost-effective and time-efficient in contrast to some other forms of probability sampling, such as simple random sampling.

3. High reliability

If clusters of the population are made properly, cluster sampling can create highly reliable/valid results as the selected sample group will mirror similar characteristics of the population.

What are the disadvantages of cluster sampling?

Like advantages, there are also quite a few disadvantages of using cluster sampling such as

1. Imprecise results with improper clusters

Results of cluster sampling can be imprecise if clusters aren’t created properly. Results are usually not as valid as those that are resulted from simple random sampling.

2. Difficult to analyze

The results are usually also difficult to compute and interpret.

3. Difficult to implement

This method of sampling also tends to be difficult to plan and execute, in comparison to some other forms of sampling.

4. High Sampling Error

Cluster sampling is also relatively more prone to high sampling error. Find out your margin of error using the margin of error calculator.

Cluster Sampling Example

Cluster sampling is more useful when a survey needs to be conducted over a larger population. When the population is larger for you to survey it as a whole, that’s where cluster sampling comes in.  

Creating cluster samples that represent the target population helps reduce bias in survey results.  

Area sampling is one example of Cluster Sampling. 

One-stage cluster sampling example 

A bakery owner is planning to expand her business. Before that, she wants to know how many people from the neighborhood buy her bakery products. She splits the neighborhood into several areas and randomly selects customers to form cluster samples. Then she surveys every member chosen from the neighborhood for her research. 

Two-stage cluster sampling example 

Let’s say the management of a toy company wants to examine how all of its outlets are performing in the market. The management divides the outlets based on their location and randomly selects samples to form clusters. Then they use the cluster sample to study the performance of all the outlets. 

[Related read: Guide to Sampling Methods]

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Cluster Sampling versus Stratified Random Sampling

Cluster sampling and stratified sampling both divide the population into subgroups. So what is the difference between the two? Here’s what:

  1. The main objective in cluster sampling is to reduce costs, while in stratified sampling the objective is to accurately represent the population and obtain results that aptly represent the population. 
  2. The subgroups in cluster sampling are called clusters, not all of these clusters are included in the sample group, and some are eliminated. In stratified random sampling, on the other hand, elements are picked from each subgroup (also known as strata) so that each strata is equally represented in the sample group.
  3. Elements from every stratum are chosen in stratified random sampling, whereas in cluster sampling, whole clusters are chosen to be a part of the sample group.
  4. Within each stratum in stratified random sampling, the sub-population is homogeneous. In contrast, each cluster has a sub-population that is heterogeneous.
  5. Stratified random sampling requires the entire population for the sampling frame, while cluster sampling only requires selected clusters.

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A cluster random sampling is a probability sampling method where researchers divide the population into clusters or smaller groups. The researcher then randomly selects among the cluster samples to conduct research and collect data. 

Cluster sample is the smaller groups made after dividing the larger population. The cluster samples represent the larger population as they share the characteristics of the entire target population. 

Cluster random sampling is used when the target population or the desired sample size is too large for the researcher to survey as a whole. 

There are three types of cluster random sampling. 

Single-stage cluster sampling: A type of cluster sampling in which each selected cluster is sampled. 

Double/ two-stage cluster sampling: Researchers collect data from a random cluster sample witing each of the selected clusters. 

Multi-stage cluster sampling: The researcher continues to randomly sample the clusters until they reach a desired or manageable sample. 

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