Cluster Sampling: Definition, Method and Examples

Cluster sampling is a data sampling method used to collect data from a group of objects close to each other. Cluster sampling is a more efficient way to collect data than randomly selecting a sample from a population. Cluster sampling is also known as stratified sampling.

One of the benefits of using cluster sampling is that it is more efficient because it collects data from a group of objects close to each other. This makes it possible to collect more data in less time. Another advantage of cluster sampling is that it is less likely to bias the survey results. This is because cluster sampling is less likely to select a sample from a population not representative of the entire population.

There are two types of cluster sampling: simple and proportional. Simple cluster sampling is a method that randomly selects a unit from a population and uses this unit to form the sampling frame. Proportional cluster sampling is a method that adjusts the sampling frame’s size to reflect the population’s size.

When using simple cluster sampling, the researcher must decide how many clusters to form. The researcher also selects a unit from each cluster. To use proportional cluster sampling, the researcher first identifies the size of the population and then determines the number of clusters. The researcher then selects a unit from each cluster based on the number of units in the cluster.

A few guidelines need to be followed when conducting a cluster sampling survey. First, it is important to determine the number of clusters used in the survey. Second, it is important to determine the size of each cluster. Third, it is important to select a random sample from each cluster. Finally, it is important to ensure that the sample is representative of the entire population.

Cluster sampling is a versatile method that can be used to collect data from various populations. It is a more efficient way to collect data and is less likely to bias the survey results.