What is cluster sampling and can you provide some examples?

What is cluster sampling and can you provide some examples?

Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of these clusters is selected. For example, if a researcher wants to study the eating habits of students in a city, they might divide the city into clusters based on schools and then randomly select a few schools to survey.

Can you give me another example of cluster sampling?

Sure! Imagine a researcher studying the health of residents in a large country. They could divide the country into regions (clusters) and randomly select a few regions to conduct health surveys, rather than surveying individuals from the entire country.

What are the advantages of using cluster sampling?

Cluster sampling can be more cost-effective and time-efficient, especially when the population is large and spread out. It also allows researchers to focus on specific areas, making data collection easier.

Are there any disadvantages to cluster sampling?

Yes, one disadvantage is that it can introduce bias if the selected clusters are not representative of the entire population. Additionally, if the clusters are too homogeneous, it may limit the variability in the data.