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Cluster sampling: Definition, application, advantages and disadvantages.

 


Cluster sampling

Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogenous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population.


Example
Area sampling

Advantages of cluster sampling

·        Consumes less time and cost
·        Convenient access
·        Least loss in accuracy of data
·        Ease of implementation

Disadvantages of cluster sampling

·        Higher sampling error, which can be expressed is the so-called “design effect”
·        Biased samples
·        Errors

Application of cluster sampling

An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a zero graphical area because a geographically dispersed population can be achieved by grouping several respondents with in a local area into a cluster.
Cluster sampling is used to estimate high mortalities increase such as wars, famines and natural disasters.

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