Cluster Sampling In Statistical Research: What It Is And When To Use It

cluster sample

a sample that is chosen by dividing the population into heterogenous groups and then choosing one or more entire group

A cluster sample is a type of sampling method used in statistical research, where the population is divided into groups or clusters based on certain characteristics. In a cluster sample, a random sample of clusters is selected and all individuals within those clusters are studied.

For example, in a research study on the health habits of a population, clusters may be defined based on their geographic location, such as zip codes. Instead of selecting individuals from every zip code, a random sample of zip codes are selected and all individuals within those zip codes are studied.

Cluster sampling is often used when the target population is large and geographically dispersed, making it difficult to identify and select a representative sample. It is more efficient compared to other types of sampling methods, as the researcher only needs to sample a limited number of clusters instead of individual participants.

However, the disadvantage of cluster sampling is that it may introduce bias into the study if the clusters are not representative of the population. It can also result in increased sampling error compared to other sampling methods, as groups within the same cluster may have similar characteristics.

More Answers:
Response Variables In Statistical Analysis: A Comprehensive Guide
Exploring Explanatory Variables: Their Impact On Research Studies And Predictive Analysis.
Effective Sampling Technique: Multistage Sampling For Large And Diverse Populations

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