Cluster sampling is a method in which the researcher develops many clusters of people from the population, and each cluster indicates homogeneous characteristics with an equal chance of being selected.

The researchers in this sampling technique analyze the sample and consider the demographics, habits, and other attributes on which they will focus. This type of sampling is used in situations where the groups are similar yet internally different from the population.

The data here are divided into small and productive groups.

Types of cluster sampling

The cluster has two distinct types. The first method decides the number of stages for obtaining the cluster sample. The second method follows the representation of groups in the entire cluster analysis. In many cases, a multiple-stage sampling procedure is followed. This technique can be divided into single-stage, two-stage, and multiple stages. These stages are explained in the following paragraphs.

Single-stage cluster sampling:

As it is clear from its name, in this type, the sampling is done only once. For example, education desires to create a sample of female students in five close towns for the provision of education. For this purpose, the department will use a single-stage cluster sampling technique and will randomly select towns (clusters) for the formation of a sample and will help female students of those towns who are deprived of education.

Two-stage cluster sampling:

In this type, the researcher chooses only some members from each group by using a systematic or simple random sampling technique. For example, a businessman desires to assess his plants’ performance, which is spread across the whole country. For this purpose, the researcher develops clusters of his plants and selects samples randomly to assess their performance.

Multiple-stage cluster sampling:

In this type, the researcher forms complicated clusters, which can be achieved through the use of multiple-stage cluster sampling. For instance, a researcher wants to conduct a survey to assess the performance of smart phones across a country. For this purpose, he or she will divide the entire population of a country into cities (cities) and then select the city with the highest population and also filter those who are using specific mobile phones.

Steps to conduct cluster sampling:

For performing this kind of sampling, the following steps are followed:

Sample:

At the first stage, the researcher decides about the target audience and the sample size.

Creation and evaluation of sampling frames:

The researcher then creates a sampling frame by either using an existing framework or creating a new one for the target audience. On the basis of coverage, clustering, and making adjustments, the frameworks are evaluated. The groups created will be varied and comprehensive in nature. The members of the sample will be individually selected.

Define the groups:

The researcher then defines the groups through the inclusion of the same members in each group. Furthermore, it made sure that the groups were distinct from each other. In other words, the groups will be mutually exclusive.

Selection of clusters:

The researcher then chooses the clusters by applying the random selection process.

Creation of sub-types:

The researcher in the last stage bifurcates them into either two-stage or multiple stage sub-types on the basis of steps followed by researcher for the formation of clusters.

Applications of cluster sampling

1. When the population being studied is too large or spread out and the study of each participant becomes costly, time-consuming, and unlikely, then cluster sampling helps.
2. With its help, smaller and more manageable population subsections with similar characteristics can be created.
3. When the population is widely spread, then, on the basis of geographical locations, sampling is done by dividing it into clusters.
4. This type of sampling is done in market research when the researcher is unable to collect information about the whole population.
5. In situations of natural calamities and wars, the cluster sampling technique works best.

Advantages of cluster sampling:

Save time and money.

Geographical segmentation and, consequently, sampling require less cost and time. So this is economical technique.

Convenient:

The convenience sampling technique can be used, which makes it convenient to increase researchers’ accessibility to numerous clusters.

Accurate data:

The data collected can be accurate because larger samples can be selected from each cluster.

Easily implemented:

There is facilitation to get information because of cluster sampling. This method can be quickly and easily implemented in practical conditions as compared to other methods of probability sampling.

Ease in deciding a group’s characteristics:

The researchers can easily decide about the characteristics of a group of people.

Has external validity:

In case of proper clustering of the population, there will be accurate reflection of the entire population.

Disadvantages of cluster sampling:

There are certain disadvantages to this technique, which are discussed below.

High sampling error:

If the cluster does not represent the characteristics of the entire population, there will be less statistical accuracy and certainty. In cases of more clustering, there is more sampling error.

Complexity:

There is a need for more planning for cluster sampling. It is because the researcher will have to decide about the manner in which he or she will divide the population.

Cluster sampling examples

When the survey is conducted for a larger population, this sampling technique is the most useful. Because of cluster sampling, there is the best possible chance of representing the population and reducing bias in the results. The examples are presented in the following paragraphs.

One-stage cluster sampling example:

Let’s take the example of a bakery owner who wants to expand his or her business. For this, the owner will first assess how many people in the surrounding area consume his or her products. The owner will split the surroundings into many areas and choose clients for cluster sampling. After that, the owner will collect information from each of the selected members in the surrounding area.

Two-stage cluster sampling example:

Let us take the example of the performance of Shoe Company in a market. The management of the company will divide the outlets on the basis of location and then select the samples for the formation of clusters. Then management will cluster samples for studying the performance of outlets.

Differences between cluster sampling and stratified sampling:

The difference between the types of sampling is explained in the following table.

Cluster sampling	Stratified random sampling
It aims to reduce costs.	Stratified sampling aims to accurately represent the population.
Clusters represent the subgroups of samples. Some clusters may be eliminated from selection.	From each subgroup, elements are selected so as to equally represent the stratum.
The whole cluster is selected as being part of the sample.	Here, a sample from each stratum is selected.
Each cluster with a sub-population is heterogeneous.	Each stratum is homogeneous in nature.
Only selected clusters are required.	It needs the whole population for the sampling frame.