The advantage of using cluster sampling is convenience and cost.However, cluster sampling is not as precise as simple random sampling or stratified random sampling.For example, Lulu wants to conduct some marketing research for Donna's campaign.
In the school, she has found that 30% of the students are involved in athletics, 25% of the students are involved in an academic club, 20% of the students are involved in an art or theater club, and 25% are involved in a music club.
None of the students are involved in more than one club and all of the students are involved in a club.
If Lulu doesn't have time to give a survey to all the students in the club, she can use two-way cluster sampling, which is a sampling method that involves separating the population into clusters, then selecting random samples from each of the clusters.
Lulu can use simple random sampling to select members of each cluster, or club, to give a survey.
We also know that cluster samples must include all members of a population, meaning that all of the students in the school must be a member of one of the four clubs.
If there are students that are not a member of one of the clubs, then the cluster sample does not work.
You may be wondering how two-way cluster sampling is different from stratified sampling.
Sometimes you won't have an entire list of the members of a population or you won't have access to an entire population.
Lulu has decided to conduct research with only the students that are in the arts, theater, and music clubs.
Since Lulu already knows that no students are involved in more than one of each of the club categories, she knows her cluster sample does not have crossover.