Populations needed to research important questions are often massive which makes it impossible to get interact with everyone or everything. Because of this, researchers use multiple sampling methods such as quota sampling, cluster sampling, and more.
Using these sampling methods, researchers can use multiple data collection methods and tools like online surveys to collect the right information. The outcome is faster results so that you can make better decisions.
In this guide, you’ll learn about cluster sampling, what it is, the methods, how to perform it the right way, and multiple advantages and disadvantages. When you’re done, you’ll know whether cluster sampling is right for you.
Cluster sampling definition
Cluster sampling can be defined as a probability sampling method where the facilitator or researcher makes multiple clusters (groups) of a population. These clusters are considered externally homogeneous but internally heterogeneous. The key part is that each cluster has an equal chance of being part of the sample used to conduct the research.
When referring to the clusters as externally homogenous it means the people within one group are similar to the people in another group. At the same time, they’re internally heterogeneous meaning within the same group, people are different.
For example, in a cluster created from the population of a city, there may be Asians, Africans, Europeans, Americans, etc. They’re all different from each other internally making them internally heterogeneous. In another city within the same country, it has a similar population of Asians, Africans, Europeans, and Americans making the cities externally homogeneous.
Cluster Sampling methods and types
There are two major types of cluster sampling methods and some schools of thought even include a third one. These are characterized by the number of stages involved in the sampling process. We’ll look at all three types of cluster sampling.
Also known as one-stage cluster sampling, this method only does sampling once so all the elements or members of a cluster enter the final sample. For example, if a company wants to reach out to high school seniors, they may randomly create clusters from a handful of schools in a city. They’ll then reach out to all the high school students contained in those clusters.
The challenge with this type of cluster sampling is that it becomes unruly with larger populations. It becomes prohibitive to add all the members of the clusters due to costs, manpower, or other factors.
In two-stage cluster sampling, the researcher creates clusters of a population and then randomly selects members or elements within that population for the research. This method is considered more feasible when larger populations are being sampled.
Continuing with the example of high school seniors. The company creates random clusters from high schools in a city. This is the first stage of sampling. In the second stage, the company pics random students from each of the clusters to reach out to. This is the second stage of sampling.
This method of cluster sampling is closely related to two-stage sampling. The major difference is that there are multiple steps instead of only two. It can be a single additional step or multiple additional steps. The choice is left to the researcher depending on their needs.
How to perform cluster sampling
1. Choose a population
This is the first and arguably most important step. The population selected will determine how well your research questions are answered. Some things to take into consideration include:
– What you’re trying to answer
– How that population perceives the research method you’re attempting to use
– How easy it is to reach the population
– The size of the population
2. Choose the groups or clusters
Settle on the number of clusters you want to create, the size of the clusters, and the criteria that are used to select these clusters. The groups should have a similar size and should also be as externally homogeneous as possible.
3. Random cluster selection
This step in the process is relatively straightforward. Each cluster is equal to a random sampling of the larger population so selecting a cluster at random is the same as random sampling. Even if the clusters aren’t fully random, the random selection will allow you to get diverse samples.
For single stage cluster sampling, you’ll use all of the members or elements within a cluster. For two stage cluster sampling, you’ll select individual members of the clusters selected.
4. Collect the data
The final step is to perform data collection and analyze the results based on the information you gain. You can then answer research questions, formulate a new hypothesis, and compare the data from other sources.
Advantages and disadvantages of cluster sampling
– Cost-effective. Since you don’t have to sample an entire population, you can cut down on costs. This benefit may be lost if the clusters are too large and you’re using single-stage cluster sampling.
– Allows researchers to take samples from multiple areas. As you may have noticed from the examples used, clusters can be taken from a wide variety of places and or communities and still have a reliable methodology to follow when doing it.
– The data samples can be as large as you want. Because you’re able to create clusters using a large population, the samples can be extremely varied and fully representative of your population. Oftentimes, this isn’t possible with other types of sampling methods. The end result is more
– There are more likely to be sampling errors. This stems from two reasons. One, the clusters may not be made properly. Two, there may be high variability within each cluster even though they’re technically representative of populations.
– More difficult to implement. Due to the nature of Cluster sampling, it’s often more difficult for researchers to implement it. It takes careful planning and consideration of populations to get true random samples. This is why there are more sampling errors within this sampling methodology.
– Cluster criteria are based on self-identified information. This isn’t always the case but clusters, especially when in a larger population, rely on the data the participants provide. This means that if participants misidentify themselves, there would be no way for the researcher to cross-check all of the members of the cluster.
Cluster sampling is one of many ways to ease the burden of data collection and research. It’s relatively straightforward to accomplish but has it’s own advantages and disadvantages.
If you’re interested in using cluster sampling, keep in mind that you can use a single stage or multiple stages to collect the right samples. This is determined by your research needs and the complexity of the population you’re working with.
Start with smaller populations then work your way up as you become more conversant. Let me know what you think in the comments and don’t forget to share.