The sampling method commonly employed when the population is scattered over a large geographic area is known as:
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Cluster sampling is a modified form of stratified random sampling used to reduce sampling costs when the population is scattered over a large geographic area.
The sampling method commonly employed when the population is scattered over a large geographic area is known as cluster sampling.
Cluster sampling is a technique used in statistics and research to select a sample from a large population that is geographically dispersed. It is often used when the cost and time involved in surveying the entire population are impractical. Instead of selecting individual elements from the population, cluster sampling involves dividing the population into clusters or groups and then randomly selecting a subset of clusters to be included in the sample.
The process of cluster sampling involves the following steps:
Population Division: The population is divided into clusters, which are typically based on geographic regions. For example, a country can be divided into states or provinces, and each state or province can be further divided into cities or towns.
Cluster Selection: A random sample of clusters is selected from the population. The number of clusters selected depends on the desired sample size and the level of precision required.
Intra-Cluster Sampling: Within each selected cluster, all the elements or a representative subset of elements are included in the sample. The specific sampling method used within each cluster can vary. For example, simple random sampling, systematic random sampling, or stratified random sampling can be employed within each cluster.
Data Collection: Data is collected from the selected clusters and analyzed to draw conclusions about the population. Statistical techniques are applied to estimate population parameters based on the data collected.
Cluster sampling has several advantages in situations where the population is scattered over a large geographic area. These advantages include:
Cost and Time Efficiency: Cluster sampling reduces the cost and time required for data collection compared to surveying the entire population. It allows researchers to collect data from a representative subset of clusters instead of visiting every location.
Geographic Representation: By selecting clusters from different geographic regions, cluster sampling ensures that the sample represents the diversity of the population across different locations.
Feasibility: Cluster sampling is particularly useful when the population is large and dispersed, making it difficult to access and survey every element individually.
However, cluster sampling also has some limitations. Since it involves sampling at the cluster level, it may introduce a greater potential for sampling error compared to other sampling methods. Additionally, if the selected clusters are not truly representative of the population, the sample may not accurately reflect the characteristics of the entire population.
To summarize, when the population is scattered over a large geographic area, cluster sampling is commonly employed. It involves dividing the population into clusters, randomly selecting a subset of clusters, and then sampling within each selected cluster. Cluster sampling provides a cost-effective way to obtain a representative sample from a geographically dispersed population.