Within the realm of research, the use of samples plays a critical role in extrapolating data about the broader population. The selection of these samples, when undertaken with precision, can significantly influence the credibility of the overall study. This guide provides an in-depth exploration of one such method known as cluster sampling. As part of our comprehensive methodology, we will delve into the intricate aspects of cluster sampling, aiding in the further understanding and application of this sampling technique.
Cluster Sampling – In a Nutshell
- Cluster sampling involves dividing a population into groups, after which the researcher can choose clusters through simple random sampling.
- This form of sampling can be done in a single stage or through multiple stages.
- A key benefit of this form of sampling is that you will end up saving money and time, particularly if the population covers a wide geographical area.
Definition: Cluster Sampling
Cluster sampling is a form of probability sampling which involves dividing a population into multiple groups known as clusters. The researchers then pick a sample randomly from the clusters to get a new study sample. This method is often used to study large populations that are distributed over a large geographical area. Since cluster sampling involves the random selection of samples, it can be used to describe the entire population.
Conducting cluster sampling
Single-stage cluster sampling only involves choosing a sample from the available clusters, and the researcher has to use all the samples within the selected clusters. This form of sampling is used when the group is homogenous in such a way that the clusters represent the population. Let’s look at the stages to follow when conducting single-stage cluster sampling.
Step 1: Defining the population
In cluster sampling, the first step is to define the population or group of individuals from which the samples will be drawn. For example, the population may be high-school students in New York.
Step 2: Divide the population into clusters
Once you have defined the population, you should divide it into clusters. It is important to take this step extremely seriously as the quality of the clusters will determine the validity of the study. When making the clusters, you should take the following factors into consideration:
- The individuals in each cluster should be diverse enough to represent the population.
- The distribution of characteristics in the cluster should be similar to those of the population.
- In total, the clusters should cover the whole population under study.
- None of the individuals should be included in more than one cluster.
If the clusters don’t work as small representations of the population, the results may not be reliable or valid. The issue with single-stage cluster sampling is that it can be hard to create perfect clusters in practical research. This is because real-life clusters are naturally-occurring groups and will usually fail to represent the population. For example, when researching high-school students in a town, it can be hard to find schools that represent the entire population. In this case, you would have to pick a number of schools. Simple random sampling generally offers higher levels of validity than single-stage cluster sampling.
Step 3: Imitate simple random sampling
Now that you have the clusters, you should assign numbers to them and choose the clusters randomly. This is similar to how you would conduct simply random sampling, and it helps to eliminate bias in the researcher. If you are able to find clusters that represent the population, this stage will help improve the validity of your results as it involves using random number generators. Simple random sampling would also be great for clusters that don’t represent the entire population. This is because the researcher would be able to study the diverse characteristics of the population.
When studying high-school students in New York, you can use simple random sampling and will pick a number of schools in the city. The ideal sample size will vary depending on population size, desired confidence levels, and the desired margin of error.
Step 4: Collecting data
The final step involves the collection of data from every cluster selected. This can be done in various ways. You can use questionnaires, interviews, surveys, observations, documents, records, and focus groups. For example, when studying the performance of students in New York, the researcher can go through the results posted by students in the selected clusters.
Multi-stage cluster sampling
With multi-stage cluster sampling, the researcher has to follow these steps:
- Define the population and create clusters
- Allocate a number to each cluster and use simple random sampling to create a sample
- From the selected clusters, you can study a number of individuals instead of the entire cluster
Depending on the nature of the study, the researcher can create smaller and smaller clusters in multiple stages. This form of sampling is commonly applied when the researcher does not have the necessary resources to test entire clusters.
When studying the performance of high-school students in New York, a researcher can create clusters of all high schools in the city. They can then pick a number of schools through simple random sampling. After that, they can narrow down the samples to a few classes. If this is still expensive or infeasible to study, the researcher can randomly select individuals from the classes.
Pros and Cons of cluster sampling
| ✓ Pros | ✗ Cons |
| Since you only have to study a few clusters of the population, it is cheaper to use this research method to study large populations. | The researcher may be biased when creating the clusters, and this would affect the overall validity of the study. |
| As a researcher, you will be able to save on administrative and travel costs, especially if the population covers a wide geographical area. | Samples drawn using this method frequently have higher sampling errors. |
| This method increases the feasibility of the study as allows researchers to divide the population into homogenous clusters. | This form of sampling is a lot harder to plan when compared to other sampling methods. |
FAQs
In statistics, cluster sampling is a technique that involves dividing a population into smaller groups known as clusters. The researcher then randomly selects samples from the clusters and studies them to form conclusions about the entire population.
The three types of cluster sampling are single-stage, double-stage, and multi-stage cluster sampling. These types of sampling differ in the number of times samples are randomly selected.
In stratified sampling, researchers aim at creating groups that are relatively homogenous when compared to the population, and the groups need to be different from each other. On the other hand, cluster sampling aims at creating groups that represent the characteristics of the population, and the clusters need to be identical.
This method is commonly used in market research and is useful in cases where the researcher cannot get sufficient information about the population as a whole.
