Cluster Sampling

In cluster sampling the units sampled are chosen in clusters, close to each other. Examples are households in the same street, or successive items off a production line.

The population is divided into clusters, and some of these are then chosen at random.  Within each cluster units are then chosen by simple random sampling or some other method.  Ideally the clusters chosen should be dissimilar so that the sample is as representative of the population as possible.

Advantages

• saving of travelling time, and consequent reduction in cost

• useful for surveying employees in a particular industry, where individual companies can form the clusters

Disadvantages

• units close to each other may be very similar and so less likely to represent the whole population

• larger sampling error than simple random sampling