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A cluster has more than one unit in it. (Some examples of clusters are schools, classrooms, and teams.)

We discuss two types of cluster sampling in the chapter, one-stage and two-stage. (Note that more stages are possible in multistage sampling; that is discussed in more advanced books on sampling.)

The first type of cluster sampling is one-stage cluster sampling.

To select a one-stage cluster sample, first randomly select a sample of clusters. 

Then you include in your final sample all of the individual units that are in the randomly selected clusters. (For example, if you randomly selected 15 classrooms you would include all of the students in those 15 classrooms.)

The second type of cluster sampling is called two-stage cluster sampling.

In the first stage you randomly select a sample of clusters (i.e., just like you did in one-stage cluster sampling).

In the second stage, you take a random sample of the elements in each of the clusters that you selected in the first stage (e.g., in stage two you might randomly select 10 students from each of the 15 classrooms you selected in stage one).

Important points about cluster sampling:

Cluster sampling is an equal probability sampling method (EPSEM) only if the clusters are approximately the same size. (Remember: EPSEM is very important because that is what produces representative samples.)

If the clusters are not the same size, you can fix the problem by using the technique called "probability proportional to size" (PPS). This will make your cluster sampling an equal probability sampling method (EPSEM), and it will, therefore, produce representative samples.

Nonrandom Sampling Techniques

The other major type of sampling used in quantitative research is nonrandom sampling. In nonrandom sampling you do not use a random sampling technique. There are four types of nonrandom sampling:

First is convenience sampling (i.e., get the people who are the most available or the most easily selected to be in your research study).

Second is quota sampling (i.e., set quotas or numbers of kinds of people you want and then use convenience sampling to meet those quotas). A set of quotas might

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