Sampling frame

Before taking a simple random sample or stratified random sample, a complete list of all individuals in the target population must be available. This is called a sampling frame.

Cluster sampling

If a complete sampling frame is not available, it may be possible to group the target individuals into reasonably small groups, called clusters, for which a complete list is available.

Clusters are similar to the strata that are used for stratified sampling, but are usually much smaller. For example, a cluster might contain all of the houses in a street, or all of the individuals in a household. It is not necessary to know beforehand how many individuals are in each of the clusters.

For cluster sampling, a simple random sample of clusters is selected, with all individuals in these clusters selected.

Cost advantages

Even when a complete sampling frame is available, cluster sampling might be used to reduce the cost of sampling (or to increase the sample size for the same cost) since it is often cheaper to record information from individuals in the same cluster than from different parts of the sampling frame.

Accuracy of cluster sampling

The disadvantage of cluster sampling is that estimates are usually less accurate than the corresponding estimates from a simple random sample of the same size.

However the cost advantages would permit a larger sample size, so cluster sampling may give the best estimates for a fixed cost.