Cluster samples

Cluster sampling is a sampling technique where the total population is divided into mutually exclusive and exhaust subgroups (clusters), a sample of the clusters is selected and all elements within a selected subgroup are measured. It is a special case of a two-stage survey design where at the second stage a sample of elements from each cluster (or first stage unit) is taken. The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. The main objective of cluster sampling is increase operational efficiency for a given cost. This contrasts with stratified sampling where the main objective is to increase precision. Because of budgetary and timing considerations, most household surveys are based on what are termed cluster samples, that is, cases where the ultimate sample units are chosen in groups of various sizes within only selected parts of the country. OECD Source: Handbook of Household Surveys, Revised Edition, Studies in Methods, Series F, No. 31, United Nations, New York, 1984, para. 4.24. When the basic sampling unit in the population is to be found in groups or clusters, e.g. human beings in households, the sampling is sometimes carried out by selecting a sample of clusters and observing all the members of each selected cluster. This is known as cluster sampling. OECD Source: A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical.