TECHNOLOGIES IN EDUCATION

Cluster Sampling

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.

Example: 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.

Definition

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.

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.

Advantages of Cluster Sampling

The cluster method comes with a number of advantages over simple random sampling and stratified sampling. The advantages include:

Requires Fewer Resources

Since cluster sampling selects only certain groups from the entire population, the method requires fewer resources for the sampling process. Therefore, it is generally cheaper relative to simple random or stratified sampling as it requires fewer administrative and travel expenses.

More Feasible

The division of the entire population into homogenous groups increases the feasibility of the sampling. Additionally, since each cluster represents the entire population, more subjects can be included in the study.

Consumes less time and cost: 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.

Disadvantages of Cluster Sampling

Despite its benefits, this method still comes with a few drawbacks, including:

Biased Samples

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

High Sampling Error

Generally, the samples drawn using the cluster method are prone to higher sampling error than the samples formed using other sampling methods.