Asked by: Ermina Oppo
asked in category: General Last Updated: 4th June, 2020

What is the difference between cluster sampling and multistage sampling?

Multistage sampling can be a complex form of cluster sampling because it is a type of sampling which involves dividing the population into groups (or clusters). Instead of using all the elements contained in the selected clusters, the researcher randomly selects elements from each cluster.

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Consequently, what is the difference between stratified sampling and cluster sampling?

The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called "strata").

Also Know, what is cluster sampling in research? Cluster sampling refers to a type of sampling method . With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters.

In respect to this, what is an example of cluster sampling?

An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a geographical area. Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster.

Why do we use multistage sampling?

It allows researchers to apply cluster or random sampling after determining the groups. Researchers can apply multistage sampling to make clusters and subclusters until the researcher reaches the desired size or type of group. Researchers can divide the population into groups without restrictions.

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