Features of cluster sampling

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pappu9268
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Features of cluster sampling

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Cluster sampling is a technique in which groups of participants that represent the population are identified and included in the sample.

The main objective of cluster sampling can be specified as cost reduction and increased levels of sampling efficiency. This specific technique can also be applied in conjunction with multistage sampling.

A major difference with stratified sampling has to do with the fact that in cluster sampling a group is perceived as a sampling unit, while in stratified sampling only specific elements of the strata are accepted as a sampling unit.

Therefore, in cluster sampling a complete list of groups represents the sampling frame. Then, some groups are chosen at random as the source of primary data.

Area or geographic sampling can be specified as the most popular version of cluster sampling. Specifically, a specific area can be divided into groups and primary data can be collected from each group to represent the viewpoint of the entire area.

The nature of cluster analysis depends on the comparative size of the norway phone number clusters individually. If there are no large differences between the cluster sizes, the analysis can be performed by combining the clusters. Alternatively, if there are large differences in the cluster sizes, probability proportional to sample size can be applied to perform the analysis.

Also learn about the characteristics of a cluster analysis.

Cluster sampling example
Imagine you want to measure how much consumers spend on various types of transport in Mexico City. Since London is a large area, we need to sample only 3 of the city's 16 boroughs.

There are three stages to applying this sampling:

Select a cluster grouping as the sampling frame. In the example above, the 16 delegations represent the sampling frame for the study.
Mark each cluster or group with a unique number. We can easily number each delegation from 1 to 16.
Select a cluster sample using probability sampling. Using systematic sampling (or any other probability sampling), we can select 3 of the 16 districts. Households residing in all 3 districts will represent the sample for the study.
Advantages of cluster sampling
Here are some of the benefits of selecting a cluster sample, also known as cluster sampling :

It is the most cost-effective and time-efficient probabilistic design for large geographic areas.
This method is easy to use from a practical point of view.
Larger samples can be used due to the level of accessibility of the sample group members.
Disadvantages
Information about the group is needed.
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