Cluster-Sampling
Cluster-Sampling is a Probability-Sampling technique used in Statistics and Social-Science-Research where the researcher divides the entire Population into separate groups, known as clusters. A random sample of these clusters is then selected, and all or some individuals within those selected clusters are surveyed. This method is frequently contrasted with Stratified-Sampling, where individuals are selected from every group.
The primary advantage of Cluster-Sampling is its cost-effectiveness and feasibility for large-scale Geographic-Sampling. For instance, if a researcher wants to study the health of high school students across a country, it is more practical to randomly select specific schools (clusters) rather than selecting individual students from every school nationwide. According to resources such as Scribbr and Investopedia, this method reduces the logistical burden and travel costs associated with Data-Collection.
However, Cluster-Sampling often leads to a higher Sampling-Error compared to Simple-Random-Sampling. This is because individuals within a cluster often share similar characteristics, leading to high Intraclass-Correlation. To mitigate this, researchers may employ Multi-Stage-Sampling, which involves further sampling within the chosen clusters to increase representativeness.