Cluster Sampling
Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. Researchers usually use pre-existing units such as schools or cities as their clusters. In cluster sampling, researchers divide a population into smaller groups known as clusters. They then randomly select among these clusters to form a sample.
How to cluster sample
The simplest form of cluster sampling is single-stage cluster sampling. It involves 4 key steps.
Step 1: Define your population
As with other forms of sampling, you must first begin by clearly defining the population you wish to study.
Step 2: Divide your sample into clusters
This is the most important part of the process. The quality of your clusters and how well they represent the larger population determines the validity of your results. Ideally, you would like for your clusters to meet the following criteria:
- Each cluster’s population should be as diverse as possible. You want every potential characteristic of the entire population to be represented in each cluster.
- Each cluster should have a similar distribution of characteristics as the distribution of the population as a whole.
- Taken together, the clusters should cover the entire population.
- There not be any overlap between clusters (i.e. the same people or units do not appear in more than one cluster).
Ideally, each cluster should be a mini-representation of the entire population. However, in practice, clusters often do not perfectly represent the population’s characteristics, which is why this method provides less statistical certainty than simple random sampling.
Because clusters are usually naturally occurring groups, such as schools, cities, or households, they are often more homogenous than the population as a whole. You should be aware of this when performing your study, as it might affect its validity.
Step 3: Randomly select clusters to use as your sample
If each cluster is itself a mini-representation of the larger population, randomly selecting and sampling from the clusters allows you to imitate simple random sampling, which in turn supports the validity of your results.
Conversely, if the clusters are not representative, then random sampling will allow you to gather data on a diverse array of clusters, which should still provide you with an overview of the population as a whole.
Step 4: Collect data from the sample
You then conduct your study and collect data from every unit in the selected clusters.
Multistage cluster sampling
In multistage cluster sampling, rather than collect data from every single unit in the selected clusters, you randomly select individual units from within the cluster to use as your sample.
You can then collect data from each of these individual units – this is known as double-stage sampling.
You can also continue this procedure, taking progressively smaller and smaller random samples, which is usually called multistage sampling.
You should use this method when it is infeasible or too expensive to test the entire cluster.
Advantages and disadvantages
Cluster sampling is commonly used for its practical advantages, but it has some disadvantages in terms of statistical validity.
Advantages
- Cluster sampling is time- and cost-efficient, especially for samples that are widely geographically spread and would be difficult to properly sample otherwise.
- Because cluster sampling uses randomization, if the population is clustered properly, your study will have high external validity because your sample will reflect the characteristics of the larger population.
Disadvantages
- Internal validity is less strong than with simple random sampling, particularly as you use more stages of clustering.
- If your clusters are not a good
- mini-representation of the population as a whole, then it is more difficult to rely upon your sample to provide valid results.
- Cluster sampling is much more complex to plan than other forms of sampling.
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