Cluster-Sampling
Cluster-Sampling is a type of sampling method used in statistics and research to estimate characteristics of a population. This technique involves dividing the population into separate groups, or clusters, which ideally represent the population as a whole. Here are detailed insights into this method:
Definition and Process
Cluster-Sampling is particularly useful when it is either impossible or impractical to compile an exhaustive list of the elements within the population. Instead, researchers:
- Divide the population into clusters, where each cluster should be a mini-representation of the entire population.
- Randomly select entire clusters from the population for inclusion in the sample.
- Either study all units within the selected clusters or take a random sample from within these clusters.
Types of Cluster Sampling
There are two main types of cluster sampling:
- Single-Stage Cluster Sampling: Here, all the elements within the selected clusters are included in the sample.
- Two-Stage Cluster Sampling: In this method, after selecting the clusters, a random sample is taken from within each selected cluster.
Advantages
- Cost-Efficiency: By focusing on clusters, travel costs and time can be significantly reduced compared to simple random sampling where each element might be spread out.
- Practicality: When the population is large and spread out, cluster sampling can be more feasible.
- Operational Ease: It simplifies the sampling process, especially in large-scale surveys.
Disadvantages
- Sampling Error: If clusters are not representative of the population, this can introduce bias and increase sampling error.
- Intra-Cluster Correlation: Units within clusters are often more similar to each other than to units in other clusters, which can affect the precision of estimates.
- Loss of Precision: Due to the homogeneity within clusters, cluster sampling can be less efficient than simple random sampling or stratified sampling if the clusters are not well chosen.
History and Context
Cluster-Sampling was first introduced in the 1930s by Jerzy Neyman who contributed significantly to statistical sampling theory. He proposed this method as a solution for efficient sampling in large populations where listing all individuals was not feasible. Over the years, it has been refined and adapted for various applications, including:
- Public health surveys
- Market research
- Educational research
- Election polls
The method gained popularity due to its practical approach in dealing with large, geographically dispersed populations, or when the population is naturally divided into groups (e.g., schools, neighborhoods).
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