Cluster Sampling
Cluster Sampling is a statistical technique used in survey methodology where the entire population is divided into groups or clusters, and a random sample of these clusters is selected for study. Each cluster ideally represents the population as a whole, allowing for efficient data collection, especially when dealing with large and geographically dispersed populations.
History
The concept of cluster sampling was first introduced by William G. Cochran in the 1940s as part of his work in survey sampling. His book, "Sampling Techniques," published in 1953, formalized many of these methods, including cluster sampling, which became a cornerstone of statistical survey design.
Context and Application
- Why Use Cluster Sampling?
- Efficiency: It reduces travel and logistical costs by concentrating data collection in selected areas.
- Practicality: When creating a complete list of the population (sampling frame) is difficult or impractical, cluster sampling provides a feasible alternative.
- Cost-Effectiveness: Particularly useful when the cost of surveying a single unit from different clusters is high.
- Types of Cluster Sampling
- Single-Stage Cluster Sampling: Here, all elements within each selected cluster are sampled.
- Two-Stage Cluster Sampling: Involves first selecting clusters, then randomly sampling elements within those clusters.
- Multi-Stage Cluster Sampling: Extends the two-stage method by possibly selecting sub-clusters within clusters.
Procedure
The steps for conducting Cluster Sampling include:
- Divide the population into clusters.
- Randomly select a number of clusters.
- If using multi-stage sampling, further sample within the chosen clusters.
- Collect data from all or a sample of the units in the selected clusters.
Advantages and Disadvantages
- Advantages
- Reduced costs and time due to localized data collection.
- Feasible for large populations spread over a wide area.
- Can be used when population lists are not available.
- Disadvantages
- Generally less precise than simple random sampling because of increased sampling error due to intra-cluster homogeneity.
- If clusters are not representative of the population, results can be biased.
- Requires careful design to ensure representativeness.
External Links for Further Reading
Related Concepts