Area sampling, also known as cluster sampling, is a sampling technique used in research and survey studies where the population is divided into clusters or geographical areas, and a subset of clusters is selected for the sample. Area sampling is particularly useful when it is difficult or impractical to create a complete list of individuals or units in the population, and when geographical proximity or clusters are relevant to the research objectives.
Here are the steps involved in conducting area sampling:
1. Define the target population: Clearly define the target population, which represents the entire group or set of individuals or units from which you want to draw your sample.
2. Divide the population into clusters: Divide the population into clusters or geographical areas based on relevant characteristics or boundaries. These clusters should be homogeneous within themselves and heterogeneous between clusters.
3. Determine the number of clusters: Decide on the desired number of clusters to be included in the sample. The number of clusters will depend on factors such as the research objectives, available resources, and the level of precision desired.
4. Randomly select clusters: Use a random selection method to choose the desired number of clusters from the population. This can involve using random number generators or random selection techniques specifically applied to the clusters.
5. Include all individuals within selected clusters: Once the clusters are selected, include all individuals or units within the chosen clusters as part of the sample. This is in contrast to other sampling techniques where only a subset of individuals within clusters is selected.
6. Analyze the data: Collect data from the selected clusters and analyze the data to draw conclusions and make inferences about the target population. Statistical techniques such as cluster-level analysis or adjustment methods may be employed to account for the clustering effect.
Area sampling allows for efficient sampling when there is a large population spread across different geographic areas or clusters. It simplifies the sampling process by selecting clusters as the primary sampling units instead of individuals or units. However, it is important to consider potential biases introduced by the clustering effect and adjust statistical analyses accordingly. Area sampling is commonly used in various fields, including social sciences, public health, and environmental studies, where geographical proximity or clusters are relevant to the research objectives.
