Let’s 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 further select cities with the highest population and also filter those using mobile devices. This multiple stage sampling is known as cluster sampling.
Cluster sampling analyzes a particular population of data in which the sample consists of multiple elements like city, family, university or school. It is used when groups that are similar yet internally diverse form a statistical population. These clusters are selected by dividing the main population into various sections.
Instead of selecting the entire population of data, cluster sampling allows the researchers to collect data by bifurcating the data into small, more effective groups.
The usual process of cluster sampling is:
- Decide a target audience and also required size of the sample.
- Create a sampling frame by using either an existing frame or creating a new one for the target audience.
- Evaluate frames on the basis of coverage and clustering and make adjustments accordingly. These groups will be varied considering the population which can be exclusive and comprehensive. Members of a sample are selected individually.
- Determine the number of groups by including the same average members in each group. Make sure each of these groups are distinct from one another.
- Choose clusters randomly for sampling.
- Geographic/Area structure is the most commonly used cluster sample.
- Cluster sampling is bifurcated into one-stage and two-stage subtypes on the basis of the number of steps followed by researchers to form clusters.
Types of cluster sampling:
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.
The first classification is the most used in cluster sampling. In most cases, sampling by clusters happens over multiple stages. A stage is considered to be the steps taken to get to a desired sample and cluster sampling is divided into single-stage, two-stage, and multiple stages.
- Single stage cluster sampling: As the name suggests, sampling will be done just once. For instance, an NGO wants to create a sample of girls across 5 neighboring towns to provide education. Using single-stage cluster sampling, the NGO can randomly select towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.
- Two-stage cluster sampling: A sample created using two-stages is always better than a sample created 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, instead of selecting all the elements of a cluster, only a handful of members are selected from each cluster by implementing systematic or simple random sampling.
- Multiple stage cluster sampling: For effective research to be conducted across multiple geographies, one needs to form complicated clusters that can be achieved only using multiple-stage cluster sampling technique. Steps of listing and sampling will be used in this sampling method.
Cluster Sampling Advantages:
- Consumes less time and cost: Sampling of geographically divided groups require 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: Large samples can be chosen with this sampling technique and that’ll increase accessibility to various clusters.
- Least loss in accuracy of data: Since there can be large samples in each cluster, loss of accuracy in information per individual can be compensated.
- Ease of implementation: Since cluster sampling facilitates information from various areas and groups, it can be easily implemented in practical situations in comparison to other probability sampling methods like simple random sampling, systematic sampling, and stratified sampling.
In comparison to simple random sampling, cluster sampling can be effective in deciding the characteristics of a group such as population and it can also be implemented without having a sampling frame for all the elements for the entire population.
Cluster Sampling Example:
A researcher inclined towards exploring the statistical performance of post-graduate students specializing in mathematics in the United States. This will surely be an enormous data that has to be divided into samples.
The researcher can create samples of students belonging to different universities to form clusters and can further bifurcate these universities according to the state they’re located in. This will be a two-level cluster sampling on which other clustering techniques like simple random sampling can be applied to proceed with the calculations.
Cluster Sampling vs Stratified Sampling:
|Cluster Sampling||Stratified Sampling|
|Elements of a population are randomly selected to be a part of groups (clusters).||The entire population is divided into even segments (strata).|
|Members from randomly selected clusters are a part of this sample.||Individual components of the strata are randomly considered to be a part of sampling units.|
|Homogeneity is maintained between clusters||Homogeneity is maintained within the strata.|
|Heterogeneity is maintained with the clusters.||Heterogeneity is maintained between strata.|
|The clusters are divided naturally.||The strata division is primarily decided by the researchers or statisticians.|
|The key objective is to minimize the cost involved and enhance competence.||The key objective is to conduct accurate sampling along with properly represented population.|
Applications of Cluster Sampling:
This sampling technique is used in an area or geographical cluster sampling for market research. A widespread geographical area can be expensive to survey in comparison to surveys that are sent to clusters which are divided on the basis of area. The sample numbers have to be increased to achieve accurate results but the cost savings involved make this process of increasing clusters attainable.
As mentioned in the application where a researcher is looking into understanding the smartphone usage in Germany. In this case, the cities of Germany will form clusters. This sampling method is used in situations like wars and natural calamities.
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