# What is Cluster Sampling Method? Purpose and Techniques

by Susan Wray 4 months ago in student
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Cluster Sampling Method

## Introduction

Sampling is a technique that enables researchers to collect data from a group of people. It makes the data collection easier because it is entirely impossible to collect data from the whole population. Sampling techniques simplify the data collection process and save time and resources for the researcher. One of the common techniques is Cluster Sampling method which is quite effective in data collection.

## What is Cluster Sampling?

Cluster Sampling encompasses sampling based on probability. Researchers categorize and divide the population into several groups. These groups are called clusters. These groups are internally heterogeneous and externally homogenous. The external homogeneity is predetermined based on a criterion of shared attributes. But the populations are heterogenous internally because the clusters within the group have different characteristics. Therefore, most of researchers prefer to hire PhD dissertation writing services in this regard.

Researchers use a random or structured random sampling approach to pick groups for data collection and analysis. Cluster Sampling is a common technique in studying large populations distributed over vast geographical landscapes. Researchers select the cluster units based on city, country, or organization.

## What are the advantages of Cluster Sampling?

The sampling technique based on clusters has the following advantages:

• Saves Time and Money

Other sample methods are more expensive and time-consuming than Cluster Sampling method. For example, a researcher who wants to collect data from a geographically scattered population sampling technique of clusters will help reduce the travel costs.

• Reliability

The data is reliable if a researcher formulates the clusters correctly. The clusters are representative of the entire population if they are divided based on internally heterogeneous and externally homogenous features. As a result, it increases the accuracy and reliability of the data.

• Convenience

This kind of sampling allows researchers to investigate enormous populations that otherwise would be too difficult or expensive to study.

## What is the Purpose of Cluster Sampling?

Collecting data from a large population is quite difficult. Cluster Sampling method aims to shrink the total number of participants that can partake in a sample study. Researchers form clusters of the population based on shared and different attributes. Researchers can use Cluster Sampling to divide the population into fewer, relatively homogeneous clusters with comparable features.

When the populations are scattered, Cluster Sampling method is especially effective in regional or geographical sampling. These clusters are representative of the entire population and resemble the attributes of the whole population. Researchers formulate the clusters by dividing the populations into sub-units and sub-populations. It makes it easier to identify and incorporate the individuals to participate in the research process.

The primary aim of the researcher using the cluster technique is to reduce expenses and save time. As a result, it increases the overall efficiency. It helps the researcher complete the research on time and within the required budget. Cluster technique is a useful strategy in market research when it is difficult for the researcher to study the entire population. It is also a useful technique in social sciences research for governmental and thinks tank research. Cluster technique enables the researchers to collect data on mortality rates from major disasters.

## What are the three types of Cluster Sampling?

Cluster Sampling encompasses three stages of sampling. Following are the three types:

• Single-Stage Sampling

• Double-Stage Sampling

• Multi-Stage Sampling

## 1. Single-Stage Sampling

The researcher permits everyone from the chosen clusters to partake in the research process using single-stage Cluster Sampling. In other words, the researcher does the sample selection only once before initiating the research. The overall sample will be divided into a set number of clusters, with the size of each cluster chosen by the researchers. Then they choose and sample clusters at random, gathering data from each member in the chosen cluster.

• It offers a huge sample size for data collecting

• It's easy to adapt your inquiries to the understanding of the lived experiences of a small cluster of people

• If you have a lot of clusters, this isn't a viable data collecting strategy.

• It has the potential to stifle the data collecting process.

## 2. Double-Stage Sampling

A researcher selects the study samples two times in a Double-Stage sampling of clusters. Initially, the researcher carries out one-stage sampling that encompasses a random sampling of the sub-populations. Furthermore, the researcher chooses only a specific number of participants from the specified clusters to participate in the research.

This technique allows the researcher to narrow down the focus of the research sample. In many cases, the sample gathered through two-stage sampling is a good reflection of the single-stage clusters' specific traits and components. But this approach is less exact than single-stage sampling. Researchers only use this method when there is a limited time and budget.

• It reduces the size of your study sample.

• It reduces the time it takes to collect data.

• The biasness of the researchers can compromise the accuracy and credibility of the data collection process

• There might be a lot of sampling mistakes

## 3. Multi-Stage Sampling

This sort of Cluster Sampling follows the same procedures as double-stage sampling but adds a few more. Researchers may proceed to pick random elements from inside clusters in multi-stage sampling until they achieve a reasonable sample size. The researcher can restrict the target population and pick a specific sample for a thorough evaluation using the multi-stage Cluster Sampling method. After determining the two-stage sample, the researcher next picks the study sample based on defined criteria.

• It allows the researcher to be more flexible. You have more time to select an appropriate sample to collect data.

• It aids in gathering primary data from a large, geographically scattered population.

• Multi-stage sampling enhances the reliability and richness of data.

• It's extremely opinionated and prone to researcher bias.

• There is no way to know if a study's conclusions are 100% reflections of the majority population.

## Conclusion

Cluster Samples are less time-consuming and cost-effective samples for a research study. When the researcher has to investigate a quite diverse and vast population cluster, samples prove effective. It enhances the credibility of the data and makes it convenient for the researcher to collect a large amount of data in a short time.

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