In cluster sampling, instead of selecting all the subjects from the entire population right off, the researcher takes several steps in gathering his sample population.
First, the researcher selects groups or clusters, and then from each cluster, the researcher selects the individual subjects by either simple random or systematic random sampling. The researcher can even opt to include the entire cluster and not just a subset from it.
The most common cluster used in research is a geographical cluster. For example, a researcher wants to survey academic performance of high school students in Spain.
The important thing to remember about this sampling technique is to give all the clusters equal chances of being selected.
Recall the example given above; one-stage cluster sample occurs when the researcher includes all the high school students from all the randomly selected clusters as sample.
From the same example above, two-stage cluster sample is obtained when the researcher only selects a number of students from each cluster by using simple or systematic random sampling.
The main difference between cluster sampling and stratified sampling lies with the inclusion of the cluster or strata.
In stratified random sampling, all the strata of the population is sampled while in cluster sampling, the researcher only randomly selects a number of clusters from the collection of clusters of the entire population. Therefore, only a number of clusters are sampled, all the other clusters are left unrepresented.