Cluster Sampling: Types, Advantages, Limitations, and Examples

In this blog, we will explain what cluster sampling is, how it differs from other common sampling methods, the types of cluster sampling available, the advantages of using it, and examples.

Table of Contents: 

• What is cluster sampling? 
• How to conduct cluster sampling in 5 steps
• Types of cluster sampling
• Advantages of cluster sampling
• Limitations of cluster sampling
• Cluster sampling vs. other sampling techniques
• Examples and applications of cluster sampling
• Research sampling with quantilope

What is cluster sampling?

Cluster sampling is a sampling technique that divides a population into groups, or, ‘clusters’. Clusters are then randomly selected to make up your total sample group for a study. This sampling technique is useful when you’re interested in surveying a large population that’s geographically dispersed, making it impractical or costly to sample every individual in the population. It’s also useful when there tends to be a natural grouping or clustering within the population, such as households, schools, or neighborhoods.

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How to conduct cluster sampling in 5 steps

Cluster sampling might sound tricky if you don’t know much about it. To make it a bit more digestible, below are 5 simple steps to go about a cluster sampling market research study:

Define your population and cluster size: Identify the population that you want to survey and determine the clusters that can be formed based on that population’s geographics, demographics, or other criteria. Then, decide how many individuals will be included in each cluster. You can do this by using statistical formulas or online calculators - but put simply, keep in mind practical considerations, such as the total desired sample size, the level of significance for accuracy, and potential variability within the clusters.

Generate your clusters: While cluster sampling is a great sampling technique, it can introduce sampling bias if the clusters are not representative of the population. When the clusters are viewed together, they should accurately represent your population as a whole. It’s also important that respondents are not included in multiple clusters (i.e. the same individual shouldn’t be a part of cluster 1 and cluster 2, for example).

Randomly select clusters: Use a random number generator (or another randomized approach) to select a random sample of clusters from your sample population. Similar to the above, the number of clusters you select will depend on the size of the population, the variance within the clusters, and the desired level of precision or representation; online tools and calculations can determine the minimum number of clusters needed to achieve this desired level of precision and significance for your total sample size.

Collect data: Now that you’ve randomly selected your clusters and have your sample ready to go, it’s time to collect data! Your data collection approach will depend on the nature of your study and the type of data you’re looking to collect. This might include online surveys for quantitative metrics, or in-person, phone, or video interviews for qualitative findings.

Analyze and interpret data: After you’ve collected your data, analyze it using statistical methods that provide weighting based on the number of individuals in each cluster. Once you’ve analyzed and interpreted the findings you can begin to draw conclusions about your chosen population.
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Types of cluster sampling 

Just when you thought you had a better idea of what cluster sampling is, here’s a new twist: there are actually two different types of cluster sampling techniques:

Single-stage cluster sampling:

Single-stage cluster sampling takes a random sample of clusters from the population and collects data from all individuals within those selected clusters. 

Two-stage cluster sampling:

Multi-stage cluster sampling divides the population into large clusters initially, but then smaller clusters are formed within those larger groups to create the final sample population. The data collected will come from these smaller groups rather than the overall larger ones (like in single-stage cluster sampling). The benefit here is that researchers can break down a larger and more dispersed population for a more representative sample. However, as you might expect, multistage cluster sampling is a longer sampling process and can be less cost-effective.
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Advantages of cluster sampling 

Below are some of the advantages of leveraging cluster sampling as your chosen sampling technique:

Cost-effectiveness:

While it can differ based on the type of cluster sampling you use (one-stage or two-stage), cluster sampling is generally a cost-efficient sampling process. It allows you to gather responses from a certain niche audience without having to pay for the whole sample to come from that audience (which can be expensive, depending on their criteria). Sample cost will of course depend on the size and distribution of your sample population, the number and size of selected clusters, and the sampling method used.

Efficiency and Speed:

It can be very time-consuming to identify and capture every individual that makes up your desired population of respondents. Cluster sampling speeds up this process by creating mini-representations of each subgroup you want to collect data from. So, instead of having to survey all college-level students in the US, perhaps you survey a ‘cluster’ of college students from each state. As you can imagine, this is a much faster approach that still provides trustworthy and representative results.

Natural groupings:

As alluded to above with our college student example, cluster sampling can be based on already naturally formed groupings in the population (i.e. neighborhoods, school districts, etc.). These natural groupings represent what the population at large tends to look like and reduces potential sampling bias by ensuring each cluster has some form of homogeneity that makes them one representative piece of the whole picture.
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Limitations of cluster sampling 

While there are plenty of benefits to cluster sampling, there are also some disadvantages to consider before opting for this market research sampling technique:

Sampling bias

There is a higher risk of bias in cluster sampling compared to other methods of sampling such as simple random sampling or probability sampling methods. Individuals within each cluster may be more similar to each other than to individuals in other clusters. This can result in an over- or under-representation of certain subgroups in the overall sample.

Complexity

Because there are several steps involved in determining and selecting each cluster, cluster sampling is considered more complex and time-consuming than other methods of sampling, particularly when multiple stages of sampling are involved (multi-stage cluster sampling).

Selection

Unlike simple random sampling where you’re surveying a general population, identifying appropriate clusters can take a bit more thought. This is particularly true when the population is not easily divided into natural groupings or clusters (like the ones mentioned in the natural groupings section above).
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Cluster sampling vs. other sampling techniques 

The table below outlines the differences between a few common sampling techniques:

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Examples and applications of cluster sampling 

As we know by this point in the blog, cluster sampling is commonly used in situations where the population is large and geographically dispersed, making it difficult and costly to obtain a complete sampling frame.

But let's now put this sampling technique into context: 

Public health studies:

Cluster sampling is great to use when studying disease prevalence or health behavior among a specific population, such as households, schools, or communities. The population can be divided into clusters based on geographic location, and a random sample of clusters can be selected for study.

Educational research:

Another example of cluster sampling is educational research, where researchers aim to study student achievement or educational outcomes. A sample of schools or classrooms can be selected, and data is captured from students within each selected cluster.

Environmental studies:

Environmental studies are another use case for cluster sampling, to collect data on air, water, or soil quality. Clusters can be defined by regions or specific types of land use (i.e. home lots, farm lots, commercial building lots, etc.). 

Political polling:

Cluster sampling can be used in political polling to study voting patterns or public opinion. Clusters can be defined by specific demographic groups, and a sample of clusters can be selected for surveying.
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Research sampling with quantilope 

quantilope is panel agnostic, meaning you call the shots when it comes to panel selection and sampling technique. If cluster sampling is one that makes sense for your business needs and available resources, simply connect your clustered sample to the platform (or reach out to a panel provider that offers this sampling technique) and start collecting data from your selected clusters. 

To learn more about other sampling techniques or what would work best for your target audience needs, get in touch below!