What is cluster random sampling?

Cluster random sampling is a sampling technique where the population is divided into clusters, and a random sample of clusters is selected for analysis.

Cluster random sampling is a method of sampling that is commonly used in large-scale studies. In this technique, the population is divided into clusters, which are groups of individuals that share some common characteristic. For example, a cluster might be a group of people who live in the same neighbourhood, attend the same school, or work in the same company.

Once the clusters have been identified, a random sample of clusters is selected for analysis. This means that each cluster has an equal chance of being selected for the study. Once the clusters have been selected, all individuals within the selected clusters are included in the study.

Cluster random sampling is often used when it is not feasible or practical to sample individuals from the entire population. For example, if a researcher wants to study the health outcomes of people living in a particular city, it may be more practical to divide the city into neighbourhoods and sample individuals from each neighbourhood, rather than trying to sample individuals from the entire city.

One advantage of cluster random sampling is that it can be more cost-effective than other sampling techniques, as it reduces the amount of time and resources required to sample individuals from the entire population. However, it is important to ensure that the clusters are representative of the population as a whole, as otherwise the results of the study may be biased.