16 Key Advantages and Disadvantages of Cluster Sampling

Cluster sampling is a statistical method used to divide population groups or specific demographics into externally homogeneous, internally heterogeneous groups. Each cluster then provides a miniature representation of the entire population. After researchers identify the clusters, specific ones get chosen through random sampling while others remain unrepresented. Then each investigator must choose the most appropriate method of element sampling from each group.

Cluster sampling typically occurs through two methods: one- or two-stage sampling. The first option requires all of the elements in selected clusters to get sampled. When researchers use the latter option, then simple random sampling happens within each cluster to create subsamples for the project.

It is essential to avoid confusing cluster sampling with the stratified approach. The latter option divides the population into mutually exclusive groups that are the reverse of this method.

When we look at the advantages and disadvantages of cluster sampling, it is important to remember that the groups are similar to each other. They simply have different internal composition.

List of the Advantages of Cluster Sampling

1. Cluster sampling requires fewer resources.
A cluster sampling effort will only choose specific groups from within an entire population or demographic. That means this method requires fewer resources to complete the research work. That’s why it is one of the cheapest investigatory options that’s available right now, even when compared to simple randomization or stratified sampling. Even when the costs of obtaining data are similar, cluster sampling typically requires fewer administrative and travel expenses.

2. It is a feasible way to collect statistical information.
The division of a demographic or an entire population into homogenous groups increases the feasibility of the process for researchers. Because every cluster is a direct representation of the people being studied, it is easy to include more subjects in the project as needed to obtain the correct level of information.

The design of cluster samples makes it a simple process to manage massive data input. It takes large population groups into account with its design to ensure that the extrapolated information gets collected into usable formats.

3. The cluster sampling approach reduces variabilities.
Every research effort creates estimates as the discovered statistics get extrapolated to the rest of the population. When investigators use cluster samples to generate this information, then the estimation has more accuracy to it when compared to the other methods of collection.

Researchers must make their best effort to ensure that each cluster is a direct representation of the population or demographic to achieve this benefit. Then the data obtained from this method offers reduced variability with its results since the findings are closer to a direct reflection of the entire group.

4. Researchers can conduct cluster sampling almost anywhere.
When resources are tight and research is required, cluster sampling is a popular method to use because of its structures. You can take a representative sample from anywhere in the world to generate the results that you want. Although geographic variability will increase the error rate in the sample by a small margin, it also opens the door to localized efforts that can still be useful to the overall demographic.

5. You receive the benefits of stratified and random sampling with this method.
Cluster sampling is a popular research method because it includes all of the benefits of stratified and random approaches without as many disadvantages. This benefit works to reduce the potential for bias in the collected data because it simplifies the information assembly work required of the investigators. Because there are fewer risks of adverse influences creating random variations, the results of the work can generate exclusive conclusions when applied to the overall population.

6. It gives researchers a large data sample from which to work.
When you work with a larger population group, then you’re creating more usable data that can eventually lead to unique findings. After researchers design and place the cluster sampling method on their preferred demographic, then similar information gets collected from each group. Investigators can then compare data points between the clusters to look for specific conclusions within a particular population group.

This advantage generates tracking data that looks at how individual clusters evolve in the future when compared to the rest of the population group. Then researchers can use that variability to understand more of the differences that can lead to a higher error rate.

7. Cluster sampling allows for data collection when a complete list of elements isn’t possible.
Cluster sampling should only be considered when there are economic justifications to use this approach. If reduced costs can be used to overcome precision losses, then it can be a useful tool. This advantage occurs most often when the construction of a complete list of the population elements is impossible, expensive, or too difficult to organize.

Instead of trying to list all of the customers that shop at a Walmart, a stage 1 cluster group would select a subset of operating stores. Then a stage 2 cluster would speak with a random sample of customers who visit the selected stores.

List of the Disadvantages of Cluster Sampling

1. Biased samples are easy to create in cluster sampling.
If the clusters in each sample get formed with a biased opinion from the researchers, then the data obtained can be easily manipulated to convey the desired message. It creates an inference within the information about the entire population or demographic, creating a bias in that segment simultaneously.

The participants of a cluster sample can offer their own bias in the results without the researchers realizing what is happening. It is a method that makes it difficult to root out people who have an agenda that want to follow.

2. There can be high sampling error rates.
The samples drawn from the clustering method are prone to a higher sampling error rate. Even when there is randomization in a two-stage process using this method, the results obtained aren’t always reflective of the general population. That’s why great care must be taken when using the statistics from a research effort such as this because there will be elements within the same population that feel completely the opposite.

3. Unconscious bias is almost impossible to detect with this approach.
Unconscious bias is a social stereotype about a specific group of people. Everyone forms this prejudice, which is also called “implicit bias,” that people hold about individuals who are outside of their conscious awareness. It is an issue that develops because of humanity’s tendency to organize our social worlds through categorizing. Because cluster sampling is already susceptible to bias, finding these implicit pressures can be almost impossible when reviewing a study.

This disadvantage boosts the potential error rate of a cluster sample study even higher. When researchers are under time pressure or must multitask when collecting information, this issue can become even more prevalent in the information.

4. Most clusters get formed based on the information provided by participants.
Cluster sampling usually occurs when participants provide information to researchers about themselves and their families. That means each group can influence the quality of the information that researchers gather when they intentionally or unintentionally misrepresent their standing. Something as simple as an artificially-inflated income can be enough to cause the error rate of the info to skyrocket.

Common areas of misrepresentation involve political preferences, family ethnicity, and employment status. If researchers only use this data to design and implement structures, then the statistical outcomes can become skewed, inaccurate, and potentially useless.

5. Cluster sampling creates several overlapping data points.
Researchers use cluster sampling to reduce the information overlaps that occur in other study methods. When you have repetitive data in a study, then the findings may not have the integrity levels needed for publication. Since clusters already have similarities because everyone gets pulled from the same population group, the levels of variability within the work can be minimal if everyone comes from the same region.

Imagine researchers are looking at families who eat fast food three times per week. What reasons do these people have when making this dining decision? If all of the individuals for the cluster sampling came from the same neighborhood, then the answers received would be very similar. That result could mean the error rate got high enough that the conclusions would get invalidated.

6. Researchers can only apply their findings to one population group.
Cluster sampling can provide a wonderful dataset that applies to a large population group. It is also essential to remember that the findings of researchers can only apply to that specific demographic. That’s why generalized findings that apply to everyone cannot be obtained when using this method. One neighborhood is not reflective of an entire city, just as a single state or province isn’t reflective of an entire country.

Researchers must have robust definitions in place when creating their clusters to ensure the accuracy of the information that gets collected. Then more structures must be in place to ensure the extrapolation applies to the correct larger specific group.

7. Cluster sampling requires size equality.
The representative samples in the clustering approach must have the same representative size to be a useful research tool. Any discrepancies in this area will create over- and under-representation in the conclusions that investigators reach with this work. If this disadvantage isn’t caught during the structuring process of the study, then data disparities are almost certain to happen. Then a significant sampling error would occur that could be challenging to identify, leading everyone toward false conclusions that seem to be true.

8. This method requires a minimum number of examples to provide accurate results.
Cluster sampling provides valid results when it has multiple research points to use. If the structure of the research includes people from the same population group with similar perspectives that are a minority in the larger demographic, then the findings will not have the desired accuracy. There must be a minimum number of examples from each perspective in this approach to create usable statistics.

When this disadvantage is present, then the risk of obtaining one-side information becomes much higher.

9. Cluster sampling requires unit identification to be effective.
The cluster sampling process works best when people get classified into “units” instead of as individuals. That’s why political samples that use this approach often segregate people into their preferred party when creating results. If investigators were to avoid this separation, then the findings could get flawed because an over-representation of one specific group might take place without anyone realizing what was happening.


The advantages and disadvantages of cluster sampling show us that researchers can use this method to determine specific data points from a large population or demographic. It doesn’t have the sample expense or time commitments as other methods of information collection while avoiding many of the issues that take place when working with specific groups.

The best results occur when researchers use defined controls in combination with their experiences and skills to gather as much information as possible. Without these tools in the toolbox, the error rate of the collected data can be high enough where the findings are no longer usable.

That’s why experienced researchers who are familiar with cluster samples are typically the people hired to design these projects. That outcome in itself can lead to implicit bias, which is why any findings generated by this process should be considered carefully.

Blog Post Author Credentials
Louise Gaille is the author of this post. She received her B.A. in Economics from the University of Washington. In addition to being a seasoned writer, Louise has almost a decade of experience in Banking and Finance. If you have any suggestions on how to make this post better, then go here to contact our team.