Have you ever thought about how researchers collect data? Especially from a large group of people or objects? Cluster sampling is a method used in statistics to gather data by dividing a large population into smaller groups or clusters.
Researchers use sampling to make inferences about the population as a whole. There are further categories of sampling methods, such as simple random, stratified, and cluster. Cluster sampling is a popular method used in research studies, particularly when the population is large and spread out.
Definition of Cluster Sampling
Cluster sampling gathers data by dividing a large population into smaller groups. The groups should be homogeneous. In terms of the characteristics under study, they should resemble each other.
For instance, the population is all the households in a city. In that case, we can define the clusters as neighborhoods.
We select the clusters using a random sampling technique. For instance, simple random sampling or systematic sampling. Then, we include all individuals or objects within the selected clusters in the sample. This is known as cluster sampling.
Types of Cluster Sampling
There are 3 main types of cluster sampling:
Single-stage cluster sampling: This type of sampling includes researchers first divide, the entire population into clusters that do not overlap. Then a sample of clusters is randomly selected. The sample includes all individuals within the selected clusters.
Two-stage cluster sampling: After selecting a sample of clusters from the first stage, we then select a sample of individuals from the selected clusters in the second stage. Large-scale surveys commonly use this method where it may be impractical or costly to sample individuals from the entire population.
Multi-stage cluster sampling: This method involves selecting clusters in multiple stages. For example, in the first stage, states are selected as clusters. Then, for the second stage, counties within the selected states.
Finally, in the third stage, selecting households within the narrowed-down counties. If the population is too big or too spread out for a representative sample, this technique can help.
Knowing which type of cluster sampling to use primarily depends on the research question. Then there is the size of the population and the availability of resources for sampling.
How Does Cluster Sampling Work?
The clusters should be similar in terms of the characteristic being studied. There are a few steps to it:
Step 1: Define the Population
Cluster sampling begins with defining the population. This could be a group of people, organizations, or geographical regions.
For example, in the case of a researcher investigating the health behaviors of American adults, the population would be all adults in the United States.
Step 2: Define the Clusters
The next step is to define the clusters. The researcher must choose a method for dividing the population into clusters. This could be by geographical region, school, workplace, or any other relevant category.
For example, if the researcher is studying the health behaviors of adults in the United States, the clusters could be individual states.
Step 3: Randomly Select Clusters
Once the clusters are defined, the researcher needs to choose a random subset of clusters for the survey. In this case, you can use a random number generator.
Even a table of random numbers works well. The researcher’s objectives determine the sample size.
Step 4: Select Sample Units
After the clusters are selected, the researcher must randomly select a sample of individuals within each selected cluster from the step above.
Step 5: Collect Data
The final step in cluster sampling is to collect the data from the selected sample units. The researcher can use various data collection methods, such as surveys, interviews, or observations. We can analyze the collected data to draw conclusions about the population.
ALSO READ: Exploratory, Explanatory, and Descriptive ResearchAdvantages of Cluster Sampling
Some of the advantages of cluster sampling are:
Cost-effective: Compared to other sampling techniques, this technique does not require a big budget. This is because it requires fewer resources to select the sample.
Time-efficient: The sample selection process takes less time, thanks to this method.
Homogeneous clusters: This ensures that the selected clusters are analogous with regard to the characteristic under research, which strengthens the reliability of the results.
Better representation: Cluster sampling provides a better representation of the population because it includes individuals from different clusters, increasing the sample’s diversity.
ALSO READ: Representative Samples: Importance + MethodsDisadvantages of Cluster Sampling
When selecting a sampling technique, one should consider the following disadvantages of cluster sampling:
Lower precision: Cluster sampling may have lower precision than other sampling techniques because it includes individuals from the same cluster, which may increase the similarity of the responses.
Higher sampling error: This type of sampling may have a higher sampling error than other sampling techniques. Since it includes individuals from the same cluster. Thus increasing the variability of the responses.
Cluster size: This requires a larger cluster size than other sampling techniques, which may increase the cost and complexity of the study.
ALSO READ: An in-Depth Study of Simple Random SamplingDifferent Industry Uses of Sampling
Many fields, such as healthcare, education, and marketing, use cluster sampling. Here are some examples of cluster sampling:
HEALTHCARE
A healthcare researcher wants to study the prevalence of a disease in a large city. They divide the city into neighborhoods and randomly select a sample of neighborhoods. Then, they collect data from all individuals within the selected neighborhoods.
EDUCATION
An education researcher wants to study students’ academic performance in a school district. They divide the district into schools and randomly select a sample of schools. Then, they collect data from all students within the selected schools.
RETAIL
A marketing researcher wants to study the buying behavior of customers in a retail store. They divide the store into sections and randomly select a sample of sections. Then, they collect data from all customers within the selected sections.
Conclusion
Data from a big population can be collected more efficiently using the statistical method known as cluster sampling. It is cost-effective, time-efficient, and better represents the population.
However, this sampling may have lower precision and higher sampling error than other techniques. Cluster sampling is suitable for research studies where the population is large and spread out, and the groups are similar to each other in terms of the characteristic being studied.
FAQs
What distinguishes stratified sampling from cluster sampling?
Stratified sampling involves dividing the population into distinct subgroups. Then select a sample of individuals from each stratum. Whereas cluster sampling divides the population into clusters and selects a sample of clusters
When is cluster sampling suitable?
Cluster sampling is suitable for research studies where the population is large and spread out, and the clusters are similar to each other in terms of the characteristic being studied.
What is homogeneous clustering?
The process of dividing the population into clusters similar to each other in terms of their characteristics is homogeneous clustering.
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