Cluster sampling
Cluster Sampling
Cluster Sampling (pronunciation: /ˈklʌstər ˈsæmplɪŋ/) is a statistical technique used in research to gather data from a population group. It is a type of probability sampling where the researcher divides the entire population into sections (clusters), then randomly selects a few of those clusters and collects data from all members of those selected clusters.
Etymology
The term 'Cluster Sampling' is derived from the English words 'cluster', meaning a group of similar things or people positioned or occurring closely together, and 'sampling', which in statistics refers to the process of selecting a subset of individuals from within a statistical population to estimate characteristics of the whole population.
Related Terms
- Probability Sampling: A sampling method in which all members of a group (population) have an equal and independent chance of being selected.
- Stratified Sampling: A method of sampling that involves the division of a population into smaller groups known as strata.
- Simple Random Sampling: A basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population).
- Systematic Sampling: A statistical method involving the selection of elements from an ordered sampling frame.
Advantages and Disadvantages
Cluster Sampling has several advantages and disadvantages. It is cost-effective and efficient, especially when the population is large and spread over a wide geographical area. However, it may not provide the most accurate representation of the population, as it can lead to a higher sampling error compared to other sampling methods.
Applications
Cluster Sampling is widely used in market research, public health studies, and other areas where the population is large and geographically dispersed. It is particularly useful when a random sample would be too difficult or costly to carry out.
External links
- Medical encyclopedia article on Cluster sampling
- Wikipedia's article - Cluster sampling
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