Cluster sampling is a scheme which is used when sampling from the whole population would be too expensive - in terms of time or money. The sample space is divided into clusters, a random sample of clusters is selected and then every member of the population within the selected clusters is studied.
For example, suppose you wanted to collect information from schools across a country and had calculated that a 5% sample was required. Rather than criss-crossing the country, you could divide the country into units: for example counties. You then select counties (your clusters) so that they cover 5% of the nation's schools. Visit each chosen county and sample all schools in it. The selection of counties would probably also need to be controlled so that urban and rural areas are properly represented.
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What is the difference between quota sampling and cluster sampling
There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.
They are an example of cluster sampling and are used because it is impractical to station interviewers at every polling place.
Advantages of cluster sampling include that it's inexpensive, fast, and simple. A disadvantage is that it is known to have a high sampling error.
Answer is Quota sampling. Its one of the method of non-probability sampling.