14 Cluster Sampling Advantages and Disadvantages

Cluster sampling is a sampling method where populations are placed into separate groups. A random sample of these groups is then selected to represent a specific population. It is a process which is usually used for market research when there is no feasible way to find information about a population or demographic as a whole.

There are 3 requirements which must be met for cluster sampling to be an accurate form of information gathering.

  1. The groups must be as heterogenous as possible, containing distinct and different subpopulations within each cluster.
  2. Each group should offer a smaller representation of what the entire population or demographic happens to be.
  3. Groups must be mutually exclusive from one another to prevent data overlaps. It should not be possible for two clusters to occur together.

Once these requirements are met, there are two types of cluster sampling which can be performed. In single-stage cluster sampling, every element in each cluster selected is used. In two-stage cluster sampling, a randomized sampling technique is used for selected clusters to generate information.

Here are the key points to consider when looking at the advantages and disadvantages of cluster sampling.

List of the Advantages of Cluster Sampling

1. It allows for research to be conducted with a reduced economy.

If you were to research a specific demographic or community, the cost of interviewing every household or individual within the group would be very limiting. By using cluster sampling, it becomes possible to compile information about certain demographics or communities by reducing the number required to generate accurate data. Although no data is 100% accurate without a complete research process of every person involved, cluster sampling gets results within a very low margin of error.

2. Cluster sampling reduces variability.

All forms of sampling create estimates. What cluster sampling provides is an estimation process that is more accurate when the clusters have been put together appropriately. Assuming each cluster is representative of the general population being researched, the information obtained through this method offered a reduced variability in its results because it is a more accurate reflection of the group as a whole.

3. It is a more feasible approach.

The ability to manage large data inputs that would be required from a complete demographic or community sampling would not be feasible for the average researcher. The design of the cluster sampling approach is specifically intended to take large populations into account. If you need to find data which is representative of a large population group, cluster sampling makes it possible to extrapolate collected information into a usable format.

4. Cluster sampling can be taken from multiple areas.

Clusters can be defined within a single community, multiple communities, or multiple demographics. The procedures used for obtaining information follow the same process, no matter how large the sample happens to be. That means researchers can generate usable information about a neighborhood by using a random sample of certain homes. They can also discover information on a large scale by approaching demographics in different areas to generate national-level results.

5. It offers the advantages of random sampling and stratified sampling.

What makes cluster sampling such a beneficial method is the fact that it includes all the benefits of randomized sampling and stratified sampling in its processes. This helps to reduce the potential for human bias within the information collected. It also simplifies the information assembly process, reducing the risks of negative influences caused by random variations. When combined, the results obtained from the sample can generate conclusions which can then be applied to the larger population.

6. Cluster sampling creates large data samples.

It is much easier to create larger samples of data using cluster samples because of its structure. Once the clusters have been designed and placed, the information being collected is similar from each cluster. That makes it possible to compare data points, find conclusions within specific population groups, and generate tracking information that can look at how different clusters evolve over time.

List of the Disadvantages of Cluster Sampling

1. It is easier to create biased data within cluster sampling.

The design of each cluster is the foundation of the data that will be gathered from the sampling process. Accurate clusters that represent the population being studied will generate accurate results. If a researcher is attempting to create specific results to reflect a personal bias, then it is easier to generate data that reflects the bias by structure the clusters in a specific way. Even if it is an unconscious bias, the data will be a reflection of the structuring, creating a false impression of accuracy.

2. Sampling errors can be a major problem.

Information collected through cluster sampling is heavily reliant on the skills of the researcher. If the information or collection methods are subpar, then the data collected will not be as beneficial as it could be. The errors found in such data would appear to be legitimate points, when in reality, they may be an inaccurate reflection of the general population. For that reason, anyone who is new to the field of research is discouraged from using cluster sampling as their initial method.

3. Many clusters are placed based on self-identifying information.

Researchers often determine cluster placement of individuals or households based in self-identifying information. That means individuals can influence the quality of the data by misrepresenting themselves in some way. All it may take to create a negative influence is a misstatement of income, ethnicity, or political preference. Inadequate structuring in the placement process by researchers can add confusion to the placement process as well. There may also be individuals who intentionally identify as a different cluster to skew research for their own purposes.

4. Every cluster may have some overlapping data points.

The goal of cluster sampling is to reduce overlaps in data, which may affect the integrity of the conclusions which can be found. When creating a cluster, however, every demographic, community, or population group will have some level of overlap on an individual level. That creates a level of variability within the data that creates sampling errors on a regular basis. In some instances, the sampling error could be large enough to reduce the representative nature of the data, invalidating the conclusions.

5. It requires size equality to be effective.

One of the primary disadvantages of cluster sampling is that it requires equality in size for it to lead to accurate conclusions. If one cluster has a representative sample of 2,000 people, while the second cluster has 1,000, and all the rest have 500, then the first two clusters will be under-represented in the conclusions, while the smaller clusters will be over-represented. That process can lead to a data disparity, which creates a large sampling error that may be difficult to identify.

6. The findings from cluster sampling only apply to those population groups.

The issue that comes up with cluster sampling is the fact that the populations they contain are only representative of that specific group. If one were to survey cities in North Carolina, for example, then the information obtained from that research could not be accurately applied to the general population of the United States. It would only be accurate for the population of the state, and even then, it may not be possible to apply findings based on regional discrepancies. That is why there must be strong definitions in place for each cluster for the research to be accurate.

7. It requires a minimum number of cases for accuracy.

Cluster sampling requires multiple research points for it to reduce the sampling errors that the research produces. Without high levels of research, the potential for data overlaps increases. There is also a higher risk of obtaining one-sided data through this process if fewer examples are taken from each cluster.

8. Cluster sampling only works well when people can be classified as units.

The processes involved with cluster sampling require people to be classified as a unit instead of an individual. That would mean they would need to be identified with a specific group, like “Republicans” or “Democrats.” If individual data points must be collected, then a different form of research is necessary.

These cluster sampling advantages and disadvantages can help us find specific information about a large population without the time or cost investment of other sampling methods. At the same time, without tight controls and strong researcher skills, there can be more errors found in this information that can lead researchers to false results. For that reason, only experienced researchers who are familiar with area sampling should use this form of research on a regular basis.