Cluster sampling exists because of the complexities that come from dealing with a large population. A target population is an important variable that makes or mars any research effort. If you’re dealing with a small target population, you can easily collect data from everyone to help you arrive at a valid result. However, this isn’t always the case.

Originally a statistical terminology, cluster sampling has become one of the most common ways to collect representative data from a vast target audience for a systematic investigation. In this guide, we’d explore different types of cluster sampling and show you how to apply this technique to market research. 

What Is Cluster Sampling? 

Cluster sampling is a survey research approach where the researcher splits the target audience into smaller naturally occurring groups or clusters and randomly chooses sets for the systematic investigation. These random selections are from the research sample for data collection and analysis. 

Cluster sampling helps researchers to study large populations. Instead of administering a questionnaire to every member of your target audience, you simply group them and collect representative data from each group. This way, the researcher speeds up the systematic investigation. By splitting the target audience into smaller, homogeneous groups, researchers achieve reliable outcomes while saving time and cost. Each group is defined by distinct characteristics—for example, gender, religion, age, and income levels.

Types of Cluster Sampling

Cluster sampling is commonly classified by stages, although some researchers prefer a classification method based on group representation in each subset. Against this background, we can identify three distinct types of cluster sampling:

1. One-stage Sampling
2. Two-stage Sampling
3. Multi-stage Sampling

One-Stage Cluster Sampling

For one-stage cluster sampling, the researcher allows every member of the selected clusters to participate in the systematic investigation. In other words, sample selection is only made once before the research takes off.

One-stage cluster sampling is also referred to as single-stage sampling. 

Examples of One-Stage Cluster Sampling

1. An organization is researching to discover how many people use its product in a community. Using single-stage sampling, the researcher splits the community into districts and randomly selects clusters to form a sample. Every member of the chosen clusters participates in the systematic investigation.
2. To know what students think about the school’s administration, the researcher chooses specific classes to provide feedback. All the students in the selected classes have the opportunity to share their views on the school’s administrative process.

Pros of One-Stage Cluster Sampling

• It provides a relatively large sample for data collection.
• It is easier to tailor your questions to the specific needs and experiences of the people in a single cluster.

Cons of One-Stage Clustering 

• It is not a feasible method of data collection if you have large clusters.
• It can slow down the data collection process.

Read: Sampling Bias: Definition, Types + [Examples]

Two-Stage Cluster Sampling

For a two-stage cluster sampling, the researcher selects the research sample twice. First, they conduct single-stage sampling where subgroups are chosen randomly. Next, they narrow down the sample by selecting a few research participants from the selected clusters. 

Most times, the final survey sample is a fair representation of distinct characteristics and elements of the single-stage clusters. 

Examples of Two-Stage Cluster Sampling

1. After selecting a particular class to participate in educational research, the teacher chooses specific students in the class to respond to survey questions.
2. As part of market research, an organization randomly selects participants from an age group within its target audience. These people form the survey sample and respond.

Pros of Two-Stage Cluster Sampling

• It narrows your research sample.
• It speeds up the data collection process.

Cons of Two-Stage Cluster Sampling

• Researcher bias can affect the validity of the data collection process.
• There can be high sampling error rates.

Multi-Stage Cluster Sampling

Multi-stage cluster sampling allows the researcher to filter the target audience and select a particular sample for the systematic investigation. After choosing the two-stage sample, the researcher further selects the research sample based on standardized criteria. 

Examples of Multi-Stage Cluster Sampling

1. During research about multilingualism in a community, the investigator uses the single-stage method to select clusters. Then, he uses the two-stage method to choose a subset within the selected groups. Finally, the researcher adopts the simple random sampling method to choose research participants from the subsets.
2. A researcher wants to know how many people earn above $5,000 a month in South America. First, he identifies specific countries for the research then narrows down to particular states within the country.

Pros of Multi-Stage Cluster Sampling

• It provides more flexibility to the researcher. You can take more time to choose a suitable sample for your data collection process.
• It helps you collect primary data from a vast, geographically dispersed population.
• Multi-stage cluster sampling improves the validity and quality of research data.

Cons of Multi-Stage Cluster Sampling 

• It is highly subjective and susceptible to researcher bias.
• Research findings can never be 100% representative of the population.

How to Conduct Cluster Sampling (Step by Step Guide)

Cluster sampling is an intelligent way to approach data collection in research. However, the success of this method depends on how well you identify homogeneous subsets within your target audience and group them accordingly. 

To help you pull through with this, here’s a simple step-by-step guide on performing cluster sampling. 

Step 1: Identify the target audience for your systematic investigation. Here, make sure the target population has adequate knowledge of the subject matter and is accessible. 

Step 2: Next, create possible sampling frames for your research. You can also adopt an existing framework for clustering and coverage. 

Step 3: Decide on the number of clusters in your target population. When you’ve done this, split the target population into clusters based on distinct characteristics. Members of the same group should have homogeneous qualities differentiating them from the others. 

Step 4: Select the clusters for your systematic investigation using any of the methods earlier mentioned. If you opt for the multi-stage method, you need to create sub-groups within each group 

Applications of Cluster Sampling 

• Cluster Sampling in Statistics

Cluster sampling is very effective in areas where researchers can’t collect data from the entire population. This makes it a very practical sampling method for statisticians undergoing research. For example, during a natural disaster, it is impractical to collect data from every single person affected by the disaster. 

• Cluster Sampling in Market Research

In market research, cluster sampling allows organizations to collect relevant responses from a vast target audience spread across multiple geographical locations.

When creating market research surveys for geographical areas, it can get pretty expensive and time-consuming to attempt to survey a broad region.

So, cluster sampling is used to survey geographical clusters divided by their respective regions. 

Stratified Sampling vs. Cluster Sampling

Stratified sampling is closely related to cluster sampling, so it’s easy to confuse one for the other. To help you, we’ve outlined four key differences between these two types of probability sampling. 

• Definition

Cluster sampling is a type of probability sampling where the researcher randomly selects a sample from naturally occurring clusters. On the other hand, stratified sampling involves dividing the target population into homogeneous groups or strata and selecting a random sample from the segments.

• Sample Type 

In stratified sampling, the research sample comprises a random selection from all strata, while for cluster sampling, the research sample comes from randomly selected clusters. 

• Bifurcation 

In stratified sampling, the researcher splits the target population into homogeneous groups. On the other hand, the sub-groups occur naturally in cluster sampling. 

• Homogeneity 

Stratified sampling achieves homogeneity within the strata, while cluster sampling achieves uniformity between the clusters. 

Read: What is Stratified Sampling? Definition, Examples, Types

Advantages of Cluster Sampling

So, why is cluster sampling a big deal in data collection? Frankly, there are several reasons. When dealing with a large target population and a strict time frame, it’s impossible to gather all the data you need from every target audience member. By adopting cluster sampling, researchers can gather quality responses from their target audience while saving time and resources. 

Common advantages of cluster sampling include: 

1. Cluster sampling is the more feasible option for gathering data from a large, sparse audience.
2. By dividing the target audience into smaller homogeneous groups, the researcher has better control over the data collection process.
3. It improves the quality of research data by providing access to diverse groups in the target population.
4. Cluster sampling reduces data inaccuracy in a systematic investigation—large clusters cover upcomprises for one-off occurrences of invalid data.
5. Cluster sampling is pretty simple to pull off, especially if you adopt the one-stage sampling approach.
6. Another advantage of cluster sampling is reduced variability. Since each cluster is a fair representation of

Disadvantages of Cluster Sampling

Although cluster sampling isn’t always the answer to data collection in a systematic investigation despite its many advantages, specifically, it has the following disadvantages: 

1. Researcher bias affects the quality of data gathered via cluster sampling. If the researcher creates subjective clusters without homogeneous characteristics, it can ruin the systematic investigation.
2. It isn’t a suitable research method for people with little experience in data collection and systematic investigation. Since it relies heavily on the researcher’s skills, any form of poor judgment can result in inaccurate research outcomes.
3. Clustering depends on self-identifying information, which puts a lot of power in the hands of the target population—any falsehood in the data will ruin the clustering and negatively impact the quality of data.
4. Sample size inequality can ruin the research process. For example, if one cluster is significantly larger than the other, they can sway the results and trigger data disparity.
5. The cluster method is prone to higher sampling error compared to other forms of sample selection in research.

Conclusion

The hack to cluster sampling is identifying the fine lines between subgroups in your research population. This means that the parameters used must create research groups that are similar yet internally diverse. You can break your target audience into naturally-occurring clusters when you get this right and collect the information you need.