What is Cluster Sampling?
Cluster sampling refers to a sampling method that has the
following properties.

Each element of the population can be assigned to one, and only one, cluster.
This tutorial covers two types of cluster sampling methods.

Twostage sampling. A subset of elements within selected
clusters is randomly selected for inclusion in the sample.
Cluster Sampling: Advantages and Disadvantages
Assuming the sample size is constant across sampling methods, cluster sampling
generally provides less precision
than either simple
random sampling or
stratified sampling. This is the main disadvantage of cluster sampling.
Given this disadvantage, it is natural to ask: Why use cluster sampling?
Sometimes, the cost per sample point is less for cluster sampling than for
other sampling methods. Given a fixed budget, the researcher may be
able to use a bigger sample with cluster sampling than with the other methods.
When the increased sample size is sufficient to offset the loss in precision,
cluster sampling may be the best choice.
When to Use Cluster Sampling
Cluster sampling should be used only when it is economically justified  when
reduced costs can be used to overcome losses in precision. This is most likely
to occur in the following situations.

Constructing a complete list of population elements is difficult, costly, or
impossible. For example, it may not be possible to list all of the customers of
a chain of hardware stores. However, it would be possible to randomly select a
subset of stores (stage 1 of cluster sampling) and then interview a random
sample of customers who visit those stores (stage 2 of cluster sampling).

The population is concentrated in "natural" clusters (city blocks, schools,
hospitals, etc.). For example, to conduct personal interviews of operating room
nurses, it might make sense to randomly select a sample of hospitals (stage 1
of cluster sampling) and then interview all of the operating room nurses at
that hospital. Using cluster sampling, the interviewer could conduct many
interviews in a single day at a single hospital. Simple random sampling, in
contrast, might require the interviewer to spend all day traveling to conduct a
single interview at a single hospital.
Even when the above situations exist, it is often unclear which sampling method
should be used. Test different options, using hypothetical data if necessary.
Choose the most costeffective approach; that is, choose the sampling method
that delivers the greatest precision for the least cost. (We will talk more about this in a
future lesson.)
The Difference Between Strata and Clusters
Although strata and clusters
are both nonoverlapping subsets of the population, they differ in several
ways.

All strata are represented in the sample; but only a subset of clusters are in
the sample.

With stratified sampling, the best survey results occur when elements within
strata are internally homogeneous.
However, with cluster sampling, the best results occur when elements within
clusters are internally heterogeneous.