Cluster Sampling: A Natural Approach of Sampling

What is cluster sampling?

Cluster sampling is defined as a sampling method where the researcher creates multiple clusters of people from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample. Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis.

In this sampling technique, researchers analyze a sample that consists of multiple sample parameters such as demographics, habits, background – or any other population attribute, which may be the focus of conducted research. This method is usually conducted when groups that are similar yet internally diverse form a statistical population. Instead of selecting the entire population, cluster sampling allows the researchers to collect data by bifurcating the data into small, more productive groups.

What are the examples of cluster sampling?

Consider a scenario where an organization is looking to survey the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and select further towns with the highest population and also filter those using mobile devices.

A researcher wants to conduct a study to judge the performance of sophomores in business education across the U.S. It is impossible to conduct a research study that involves a student in every university. Instead, by using cluster sampling, the researcher can club the universities from each city into one cluster. These clusters then define all the sophomore student population in the U.S. Next, either using simple random sampling or systematic random sampling or randomly picking clusters for the research study. Subsequently, by using simple or systematic sampling, the sophomore’s from each of these selected clusters can be chosen on whom to conduct the research study.

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What are the types of cluster sampling?

Types of cluster sampling

There are two ways to classify this sampling technique. The first way is based on the number of stages followed to obtain the cluster sample, and the second way is the representation of the groups in the entire cluster. In most cases, sampling by clusters happens over multiple stages. A stage is considered to be the step taken to get to the desired sample. We can divide this technique into single-stage, two-stage, and multiple stages.

Single-stage cluster sampling:

As the name suggests, sampling is done just once. An example of single-stage cluster sampling – An NGO wants to create a sample of girls across five neighboring towns to provide education. Using single-stage sampling, the NGO randomly selects towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.

Two-stage cluster sampling:

Here, instead of selecting all the elements of a cluster, only a handful of members are chosen from each group by implementing systematic or simple random sampling. An example of two-stage cluster sampling – A business owner wants to explore the performance of his/her plants that are spread across various parts of the U.S. The owner creates clusters of the plants. He/she then selects random samples from these clusters to conduct research.

Multiple stage cluster sampling:

Multiple-stage cluster sampling takes a step or a few steps further than two-stage sampling. For conducting effective research across multiple geographies, one needs to form complicated clusters that can be achieved only using the multiple-stage sampling technique. An example of Multiple stage sampling by clusters – An organization intends to survey to analyze the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and select cities with the highest population and also filter those using mobile devices.

Applications of cluster sampling

This sampling technique is used in an area or geographical cluster sampling for market research. A broad geographic area can be expensive to survey in comparison to surveys that are sent to clusters that are divided based on region. The sample numbers have to be increased to achieve accurate results, but the cost savings involved make this process of rising clusters attainable.

Cluster sampling in statistics

The technique is widely used in statistics where the researcher can’t collect data from the entire population as a whole. It is the most economical and practical solution for statisticians doing research. Take the example of a researcher who is looking to understand smartphone usage in Germany. In this case, the cities of Germany will form clusters. This sampling method is also used in situations like wars and natural calamities to draw inferences of a population, where collecting data from every individual residing in the population is impossible.

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What are the advantages & disadvantages of Cluster Sampling?

The advantages of Cluster sampling are

Consumes less time and cost

A sampling of geographically divided groups requires less work, time, and cost. It’s a highly economical method to observe clusters instead of randomly doing it throughout a particular region by allocating a limited number of resources to those selected clusters.

Convenient access:

Researchers can choose large samples with this sampling technique, and that’ll increase accessibility to various clusters.

Data accuracy:

Since there can be large samples in each cluster, loss of accuracy in information per individual can be compensated.

Ease of implementation:

Cluster sampling facilitates information from various areas and groups. Researchers can quickly implement it in practical situations compared to other probability sampling methods.

With so many advantages, there are a few Disadvantages of Cluster Sampling that are given below-

Biased samples

The method is prone to biases. If the clusters representing the entire population were formed under a biased opinion, the inferences about the entire population would be biased as well.