In this blog, learn what cluster sampling is, types of cluster sampling, advantages to this sampling technique and potential limitations.
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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.
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.
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 takes a random sample of clusters from the population and collects data from all individuals within those selected clusters.
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.
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:
Begin by clearly identifying the specific population you want to study through market research. Maybe it's college students in a particular state, customers of a certain product, or residents of a specific region. Once your overall population is defined, think about the clusters that might make sense within it - geographic location (like cities and neighborhoods), demographics (such as age groups or income levels), or other criteria specific to your research question (like school districts or types of businesses).
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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Below are some of the advantages of leveraging cluster sampling as your chosen sampling technique:
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.
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.
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.
While there are plenty of benefits to cluster sampling, there are also some disadvantages to consider before opting for this market research sampling technique:
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.
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).
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).
The table below outlines the differences between a few common sampling techniques:
Cluster
sampling
Systematic sampling
Stratified
sampling
Simple random sampling
Population
Population is divided into clusters or groups
Population is ordered in some way
Population is divided into strata or subgroups (like cluster sampling)
Whole population is considered
Sampling unit
Clusters are selected randomly, but the entire population of selected cluster is surveyed
Every n’th unit in the population is selected for surveying (i.e. every 10th individual)
Individuals within each subpopulation are randomly selected for surveying
Individuals are randomly selected from the population for surveying
Homogeneity within the sample unit
High homogeneity within each selected cluster
Assumes homogeneity within selected intervals
Lower homogeneity within each stratum/subgroup
Assumes homogeneity across the entire population
Complexity
Fewer stages of sampling involved
Simple to implement with one-stage sampling
More stages of sampling involved
Simple to implement with one-stage 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:
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.
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 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.).
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.
Cluster sampling is an efficient way to gather data from large, dispersed populations - especially when natural fallout groups tend to exist within it.
Below are three specific examples of how a market research or insights team might leverage cluster sampling for their unique business objectives.
A large fast-food chain wants to assess customer satisfaction and preferences across its numerous locations in the United States.
Conducting individual surveys with every fast-food chain customer would be incredibly time-consuming and expensive. Instead, they opt to use cluster sampling. The fast-food chain divides the country into regions (clusters): Northeast, Midwest, South, and West. Within each region, they randomly select a number of cities. Then, within each chosen city, they select a few specific restaurant locations. Finally, they survey customers at those chosen locations.
This multistage cluster sampling approach allows the fast-food chain to efficiently gather data about their customers while still getting a representative picture of customer sentiment across the entire country.
As another example, let's consider a smartphone manufacturer that's interested in how different age groups use their devices. From the start, the smartphone manufacturer knows that different age groups likely interact with a smartphone in different ways (i.e. Gen Z vs. Gen X), creating natural clusters within the population (e .g., 18-24, 25-34, 35-44, etc. ).
From each age cluster, they randomly select a sample of individuals and conduct in-depth surveys or interviews to understand consumers' smartphone usage habits, preferences, and pain points. This targeted approach allows the smartphone manufacturer to make smarter business decisions around new product development, marketing strategy, and user experience improvements.
Finally, let's say a major airline wanted to evaluate the effectiveness of their new in-flight entertainment system across their entire fleet. Surveying every passenger on every flight wouldn't be a feasible approach. Instead, they opt to use cluster sampling.
They group flights into clusters based on origin and destination cities, flight duration, and time of day. From this diverse set of clusters, they randomly select a subset of flights for further study. Passengers on these selected flights are surveyed about their satisfaction with the new entertainment system, its features, and any suggestions for improvement.
This cluster sampling approach allows the airline to gather comprehensive feedback from a wide range of passengers without having to survey every individual flyer, enabling them to make data-driven decisions about their in-flight entertainment offerings.
As a summary, cluster sampling is a valuable technique in research, especially when dealing with large and diverse populations. It involves dividing a population into smaller groups ( clusters) and then randomly selecting a subset of those clusters to represent the entire population. Researchers often favor cluster sampling due to its efficiency and practicality.
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!
