Cluster sampling: What it is and when to use it

Cluster sampling is one of the most common sampling methods. In this approach, researchers divide their research population into smaller groups known as clusters and then randomly select some of these clusters as their sample. 

Cluster sampling is widely used in fields such across market research, education, and healthcare studies as it’s an efficient and cost-effective methodology if you’re looking to research a large population. 

If you’re curious about the answer to questions like, “What is a cluster sample?”, “What are the pros and cons of cluster sampling and when should I use it?” and, “How does cluster sampling compare to other sampling methods?” then this post is for you.

Here are the main terms you need to know when it comes to cluster sampling. 

Cluster

A cluster refers to a group of research participants, taken and divided from the entire research population by a researcher. Clusters are typically randomly selected. 

Cluster sampling

Cluster sampling is the process of collecting data for a large research population by breaking down that population into small groups known as clusters. 

In traditional statistics, clusters are groups of data points that are grouped based on their similarities to each other in comparison to other data points in other groups. Clusters are built around shared characteristics or features. Researchers often use a research analysis technique called cluster analysis to pinpoint and separate datapoints into each unique cluster. 

In comparison, the term clusters in cluster sampling is used to describe groups or clusters that naturally  occur within a wider population 

It’s also important to note that there are different types of cluster sampling techniques. Single-stage cluster sampling is when a simple random sample of clusters is chosen by researchers, who then survey everyone in those clusters.  

Double-stage cluster sampling is when researchers survey a sample of people from each cluster. This type of cluster sampling can be a plus if you’re researching a larger population and want to save time.

In cluster sampling, researchers divide a research population into smaller groups or clusters (these are typically naturally occurring groups). They then randomly select a subset of these clusters to study in their research. 

Cluster sampling falls under the label of a probability sampling method. Probability sampling methods refer to a type of sampling technique that researchers use to randomly select a smaller sample of a research population. These sampling methods give every member of a research population the chance of being included in a sample for study. In the case of cluster sampling, this approach helps make sure that each cluster is representative of the diversity within the entire research population. 

Beyond cluster sampling, there are three other main probability sampling methods: stratified sampling, random sampling and systematic sampling. Here’s how they compare: 

1. Simple random sampling: A researcher randomly selects a subset of the statistical population. 

2. Stratified sampling: A researcher divides the population into subpopulations (called stratas) based on the populations; various demographic factors like gender, age, or level of education.

3. Random sampling: A researcher selects a subset of research participants using a tool like a random number generator. A random number generator is a tool that researchers use to assign numbers to members of the population and then randomly selects from the list of numbers to choose who will be included in the sample. 

Let’s take a look at the main advantages and disadvantages of cluster sampling. 

Benefits of cluster sampling

Cluster sampling is a great method to use if you want to study large populations — making it simpler, easier and more time efficient to study large groups of people. This example, from Lisa Stowe, shows how cluster sampling can help simplify research projects: 

“The highrise houses a total of 300 renters on its 15 floors since the units are all roughly the same. We can deduce there are about 20 renters on each floor. 

If we cluster the population of the highrise into clusters based on floor number, we will create manageable groups that can quickly and easily be surveyed. The rental units are all similar meaning any given floor should be a good representation of the people on any other floor.”

As clusters are typically created from naturally-occurring groups, as in the above example, they’re often simple to conceptualize and easy to create. Take stratified sampling by comparison. For this sampling method, researchers need to divide the population into strata based on shared characteristics that are most relevant to the study (such as race, age or gender) and then randomly sample each subgroup. This can be a lengthy and time-consuming process when dealing with multiple strata. 

Cluster sampling is also often more affordable than the sampling methods we cover above, like simple random sampling. By focusing their research within a small number of specific clusters rather than attempting to involve people across the entire research population, researchers can cut costs by saving on things like travel expenses and extensive data collection and analysis. 

Disadvantages of cluster sampling 

Let’s explore some of the main disadvantages of cluster sampling. 

Homogeneity within clusters 

“One of the main disadvantages of cluster sampling is that it can introduce bias and error in the data. By sampling clusters, you may lose some information and variability that exists within the population. This can lead to lower precision and higher standard errors in your estimates.” Sohag Maitra, Senior Consultant – Data Analytics at I3GlobalTech Inc

One of the biggest issues of cluster sampling is the risk of bias — with cluster sampling possessing a higher risk of bias than other sampling methods such as simple random sampling. This is down to the potential for homogeneity within clusters. If research participants are too similar to each other, then this may mean that research samples are not representative of the wider population. 

Say a brand wants to research consumers’ perceptions of a popular bike model. They select research participants from cities with a high number of cyclists. However, these cities also have a high number of young students with low incomes. This homogeneity can bias their results. 

Selection bias 

Cluster sampling is also vulnerable to selection bias. This means that researchers are more likely to select certain research participants for their clusters than others. For example, researchers may inadvertently select research participants that are more accessible e.g. students with free time during the day or people who live closer if they’re undertaking in-person research. 

Here’s how to help decrease biases in cluster sampling: 

1. Increase heterogeneity within clusters: Make sure your clusters include a diverse range of research participants. 

2. Random selection: Use random methods to choose clusters with tools like random number generators. 

3. Use two-stage sampling: Make use of two-stage sampling — first, select a simple random sample of clusters. Next, choose a simple random sample from the units in your sampled clusters.

Cluster sampling is a brilliant sampling method in particular research scenarios. Let’s take a look at when to use this method. 

It’s a smart move to use cluster sampling when you’re looking to research a large and geographically-dispersed population. As it’s impractical and time-consuming, or even downright impossible, to reach every single person within a large research population, cluster sampling makes researching these groups possible by breaking them down into smaller groups or clusters that are easier to study.  

Cluster sampling is also a great choice of sampling method if you’re dealing with budget or logistical constraints. By allowing teams to focus on specific clusters in specific locations, rather than a wider research population, cluster sampling can dramatically cut down on the expenses of travel expenses, data collection and personnel. 

Cluster sampling is also an ideal sampling method when you need to collect specific group-level data, such as when you’re interested in understanding more about specific market segments. For example, a sportswear brand may use cluster sampling to identify neighborhoods with a large percentage of young parents or middle-income professionals.

Here are the main steps to follow to develop a cluster sample. 

1. Define the population

The first step is to define your research population. Who do you want to study? Maybe it’s consumers from a particular ethnic background, consumers who have certain hobbies or those who live in a specific neighborhood. 

Once you have your broader research population defined, consider which clusters could be created from this particular group of consumers. For example, say you’re looking to get insights into consumers who live in a particular neighborhood. You may consider clusters based on their age, income level, household size, level of education, gender and ethnicity. 

2. Divide the population into clusters

The second step is to divide your research population into clusters. 

Some potential clusters you may explore include: 

• Geographical locations such as local cities, neighborhoods or districts. 

• Psychographics such as personality characteristics, attitudes, lifestyles or values. 

• Organizational structures such as company franchises, schools in a catchment area or different levels of an organization. 

• Sociodemographic characteristics like age, gender, ethnicity or income levels.

As we alluded to above, it’s important to pinpoint groups that naturally occur and are a great representation of the entire population. Think: A range of ages, ethnicities, professions, income levels, genders and political or religious beliefs. 

3. Select clusters using random sampling

Once you’ve created your clusters, use random sampling techniques to randomly select the clusters for your sample. As we note above, random sampling is important for helping to reduce selection bias as each cluster has the same probability of being included in your sample. As one prospective method, you could assign a number to each cluster and then use a random number generator to choose your sample. 

4. Collect data from all members of the chosen clusters or a subset

Now you know who you’ll be collecting your data from, it’s time to survey or interview them. You can choose to collect data from every unit in your chosen cluster or a subset. 

Let’s take a look at a real-world example of cluster sampling in market research:

A local beer company wants to find out what consumers in the city think about a new product line. 

Step 1: Define clusters

First, the company needs to define their clusters. They build their clusters around the different neighborhoods across the city. Neighborhood A, B, C and more become their own separate clusters for study. 

For each cluster, the research team observes different characteristics. The demographics differ across each neighborhood — with some neighborhoods with high-income families and several high-end bars and restaurants, others with middle-income young professionals and with low-income students and a high number of affordable bars.

By using a diverse selection of neighborhoods for their clusters, the researchers help increase the likelihood that their sample will be representative of their research population.  

Step 2: Randomly select clusters

To help ensure representativeness and reduce bias, the team randomly selects their clusters for study. To do this, they use random number generator software, randomly assigning each of their clusters a number and using the tool to randomly select several of these numbers for the sample. 

Using a random number generator or a similar method, the company selects a random sample of these neighborhoods. If there are 40 neighborhoods then the random number generator may select 10 at random for the study. 

Step 3: Two-stage sampling

After selecting their initial clusters, the team uses their RNG to randomly choose another set of clusters from each neighborhood. 

Step 4: Data collection

Now they're ready to collect their data. The researchers use a range of surveys and focus groups. The surveys give them a rich source of broad data on the consumers within each cluster's perceptions on their brand, beer and those of their competitors. 

The focus groups include a taste test of both the current product and the new beer line. The focus groups allow the team to get deeper insights into consumers' taste perceptions, opinions and feelings about the new line and product. 

Step 5: Analysis

The team then analyzes their results to get a deeper understanding of consumers.

To do this, if they’ve run their survey on an online platform like Zappi, for example, they have the ability to centralize as well as analyze their research data across every cluster. They can also use the Zappi platform to segment their audience based on their clusters, generate statistical insights for comparison across clusters and create charts and graphs to help them more easily pick out patterns and differences between each.  

How does cluster sampling compare with the other main probability sampling methods we covered above? 

With simple random sampling, the strengths of the method are the reverse of the weaknesses of cluster sampling. Simple random sampling is typically less prone to bias as every member of a research population is equally as likely to be chosen as someone else.

It’s also an appealing approach due to its simplicity. In cluster sampling, researchers first need to separate a population into clusters before creating their samples. Simple random sampling does not require this additional step, allowing researchers to collect data more quickly.

In contrast, this method can sometimes be difficult to implement. And despite the fact that the method is technically faster, gaining access to population information can often make it more time consuming. 

For example, researchers may not be able to access a complete list of the population. In these cases, they’ll need to spend time gathering information on a population from different organizations. This can be expensive and time consuming. 

Stratified sampling is a great method for helping to ensure a sample is representative of a population. Because stratas (small groups) are created based on the population’s key characteristics, such as age, ethnicity, and income and a random sample taken from each group, the diversity of a research population is typically accounted for. This makes it a fitting sampling method for diverse populations.

On the downside, it can be a more complicated and time-consuming method than cluster sampling as pinpointing and creating each strata can be a lengthy process. Researchers also need to be able to accurately identify relevant characteristics and categorize people accordingly. If they don’t feel confident in their ability to do so, this undermines the accuracy and reliability of the method. In these cases, cluster sampling can be used to help researchers study larger, dispersed populations. 

Cluster sampling is regularly used across a range of industries including healthcare, education, psychology and consumer insights. It’s a great methodology for companies and researchers looking to understand larger, dispersed populations and make studying them more efficient and affordable.

Take the healthcare sector, for instance. Rather than studying an entire geographic area,  patients may be separated into manageable clusters such as those who attend a particular doctor’s office or hospital. 

At Zappi, we believe that sample consistency is essential in consumer insights — reducing bias, improving accuracy and delivering richer data. In line with cluster sampling’s philosophy, we use a consistent blend of sample sources by product and country; this mirrors the samples used in cluster sampling. 

Cluster sampling is a great sampling method for helping businesses and researchers sample and collect data from large, dispersed populations in an efficient way. Understanding cluster sampling in the context of the other main probability sampling methods can help you understand when this method is the right approach for you. 

As with all sampling methods, cluster sampling needs to be carefully planned to help make sure your samples are representative and have been selected with as little bias as possible. It’s essential to be aware of both the benefits and potential drawbacks of this method so you can factor them in and address them along the way.