Cluster Sampling

Cluster Sampling: Definition

Cluster sampling is a sampling technique that divides the main population into various sections (clusters). In this sampling technique, analysis is carried out on a sample which consists of multiple sample parameters such as demographics, habits, background – or any other population attribute which may be the focus of conducted research. It is usually used when groups that are similar yet internally diverse form a statistical population.

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.

Although it is now a commonly used term, if you’re not familiar with “cluster sampling”, it can come across as a bad episode from the Twilight Zone. There’s something eerie about the word to make one feel like he/she is about to get probed by aliens. Ok, perhaps it is just me and my wild imagination.

Unless you’re 70 years old, cranking stats in a windowless basement office, market research terminologies can make even the brightest of the bunch take a mulligan or spurt an occasional “what the heck?”. Ironically, the term cluster is probably one of the most simplistic terminologies in the research dictionary.  A cluster, as defined by Webster, is “a group of similar things or people positioned or occurring closely together”. The main idea behind creating clusters is to group a set of objects in order to create relationships and similarities within each group. In a nutshell, it is simply a form of classification or distributing groups.

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.  

The usual process of cluster sampling is:

  • Decide a target audience and also required the 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 segmentation 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.  

Cluster Sampling Methods with Examples

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. An example of Single Stage Cluster Sampling –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 samplingAn example of Two-Stage Cluster Sampling –A business owner is inclined towards exploring the statistical performance of her plants which are spread across various parts of the U.S. Considering the number of plants, number of employees per plant and work done from each plant, single-stage sampling would be time and cost consuming. This is when she decides to conduct two-stage sampling. The owner creates samples of employees belonging to different plants to form clusters and then divides it into the size or operation status of the plant. A two-level cluster sampling was formed on which other clustering techniques like simple random sampling were applied to proceed with the calculations.
  • 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. An example of Multiple Stage Cluster Sampling –Geographic cluster sampling is one of the most extensively implemented cluster sampling technique. If an organization intends to conduct a survey to analyze 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.

Why Cluster Sampling?

In an ideal world, research practitioners would love to survey the entire population and select their respondents randomly to make sure everyone is accounted for and therefore ensure their research results are as accurate as possible. This is referred to as random sampling. Unfortunately, there are two issues associated with this approach – cost and feasibility. However, by dividing and classifying the population into groups (cluster sampling), this provides the researcher the ability to account for individuals with a common interest, relative to the larger population. By using the cluster sampling technique, the sample data set is smaller, which helps keep research costs reasonable.

When using cluster sampling methods, it is critical to keep in mind that only one variable (element) can be assigned to a cluster. In most cases, clusters are created by geography. For example, if Apple wanted to gauge the performance of the iPad in Spain, the researcher would create clusters by all cities in Spain. The larger cities would be accounted for and cluster analysis would determine the usage of iPad by each city.

While there are other complexities to using cluster sampling – stages, sample selection, sample size, etc., in comparison to other sampling methods, cluster sampling can be a very effective technique to determine the characteristics of a group and can be implemented without the need of other elements of the population. Most importantly, cluster sampling provides 4 key advantages that other methods fall short on:

  • Convenience
  • Takes less time and cost less
  • Easy to implement
  • Higher margin on data accuracy

The next time you’re limited by budget and don’t have time to run around the country to conduct interviews, consider using cluster sampling. Getting probed is the least of your worries.

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 such as simple random sampling, systematic sampling, and stratified sampling or non-probability sampling methods such as convenience 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 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.

Learn more: Cluster Sampling vs Stratified Sampling

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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