Clustered Sampling:

A sampling technique which divides the main population into various clusters which consist of multiple sample parameters like demographics, habits, background or any other attribute.Cluster sampling allows the researchers to collect data by bifurcating the data into small, more effective groups instead of selecting the entire population of data.

There are two ways to classify cluster sampling. 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.

Cluster sampling can be classified as single-stage, two-stage, and multiple stages (in most of the cases).

Single Stage Cluster Sampling:

Here, sampling will be done just once.

Example:

An NGO wants to create a sample of girls across 5 neighboring towns to provide education. To form a sample, The NGO can randomly select towns (clusters). Then they can extend help to the girls deprived of education in those towns directly.

Two-Stage Cluster Sampling: 

A sample created using two-stages is always better than using a single stage because more filtered elements can be selected which can lead to improved results from the sample. In two-stage cluster sampling, by implementing systematic or simple random samplingonly a handful of members are selected from each clusterinstead of selecting all the elements of a cluster.

Example:

A business owner wants to explore the statistical performance of her plants which are spread across various parts of the state. Based on the number of plants, number of employees per plant and work done from each plant, single-stage sampling would be time and cost consuming. The owner thus creates samples of employees belonging to different plants to form clusters and then divides it into the size or operation status of the plant.

Multiple Stage Cluster Sampling: 

For A research to be conducted across large multiple geographies, one needs to form complicated clusters that can be achieved only using multiple-stage cluster sampling technique.

Example:

If an organization intends to conduct a survey to analyze the performance of smartphones across India. They can divide the entire country’s population into states (clusters) and further select cities with the highest population and also filter those using mobile devices.

Steps to form a Clustered sample:

Form a target audience and required size of the sample.

Create a sampling frame by using either an existing frame or by creating a new one for the target audience.

Evaluate frames on the basis of coverage and clustering considering the population which can be exclusive and comprehensive.

Determine the number of distinct groups by including the same average members in each group.

Randomly choose clusters for sampling (mostly clusters are created by geography).

Advantages:

Sampling of groups divided geographically require less work, time and cost.

Ability to choose larger samples which will increase accessibility to various clusters.

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

Since cluster sampling facilitates information from various areas and groups, it can be easily implemented in practical situations in comparison to other sampling methods.

Disadvantages:

Requires group-level information to be known prior.

Commonly has higher sampling error than othersampling techniques.

Even though bothstrata and clusters are non-overlapping subsets of the population, they do differ in several ways.

All strata are represented in the sample; but only a subset of clusters are in the sample.

With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. Whereas, with cluster sampling, the best survey results occur when elements within clusters are internally heterogeneous.