Definition:

Cluster sampling is a probability sampling technique in which the population is divided into distinct groups, known as clusters, and a random sample of clusters is selected for further analysis. Each selected cluster represents the entire population, and all individuals within the chosen clusters are included in the sample.

How Cluster Sampling Works:

1. Population Identification: The first step is to identify and define the target population for the study.

2. Cluster Formation: The population is then divided into clusters based on certain characteristics or geographical regions.

3. Cluster Selection: A random sample of clusters is chosen from the list of available clusters.

4. Within-Cluster Sampling: After selecting the clusters, all individuals within the chosen clusters are included in the sample.

Advantages of Cluster Sampling:

– Time-efficient and cost-effective method, especially when dealing with a large population.

– Suitable for widely dispersed populations or areas with geographical clustering.

– Reduces data collection and processing efforts by sampling entire clusters.

Disadvantages of Cluster Sampling:

– Potential lack of representativeness if clusters are not homogeneous.

– Increased variability due to cluster-level similarities.

– More complex analysis techniques compared to other sampling methods.