Cluster sampling

Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population((‘Cluster sampling—Wikipedia’, n.d.) .

Figure 1.cluster sampling

In this sampling technique, analysis is carried out on a sample which consists of multiple sample parameters such as demographics, habits, background – or any other population attribute which may be the focus of conducted research. This method is usually conducted when groups that are similar yet internally diverse form a statistical population. Instead of selecting the entire population of data, cluster sampling allows the researchers to collect data by bifurcating the data into small, more effective groups( (Phillips, 2015).

Another example of this would be; let’s consider a scenario where an organization is looking to survey the performance of smartphones across Germany. They can divide the entire country’s population into cities (clusters) and further select cities with the highest population and also filter those using mobile devices. This multiple stage sampling is known as cluster sampling.

cluster sampling from kpsilpa

cluster sampling from kpsilpa