Cluster sampling is a sampling technique used when "natural" groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. Sampling may refer to: Sampling (signal processing), converting a continuous signal into a discrete signal Sampling (music), re-using portions of sound recordings in a piece Sampling (statistics), selection of observations to acquire some knowledge of a statistical population Sampling (case studies), selection of cases for single or multiple case... In statistics, a statistical population is a set of entities concerning which statistical inferences are to be drawn, often based on a random sample taken from the population. ... A sample is that part of a population which is actually observed. ...

Elements within a cluster should ideally be as heterogeneous as possible, but there should be homogeneity between cluster means. Each cluster should be a small scale representation of the total population. The clusters should be mutually exclusive and collectively exhaustive. A random sampling technique is then used on any relevant clusters to choose which clusters to include in the study. In single-stage cluster sampling, all the elements from each of the selected clusters are used. In two-stage cluster sampling, a random sampling technique is applied to the elements from each of the selected clusters. In statistics, mean has two related meanings: the arithmetic mean (and is distinguished from the geometric mean or harmonic mean). ...

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 to reduce costs by increasing sampling efficiency. This contrasts with stratified sampling where the main objective is to increase precision. In statistics, stratified sampling is a method of sampling from a population. ...

One version of cluster sampling is area sampling or geographical cluster sampling. Clusters consist of geographical areas. As geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by treating several respondents within a local area as a cluster. It is usually necessary to increase the total sample size to achieve equivalent precision in the estimators, but cost savings may make that feasible. In statistics, an estimator is a function of the observable sample data that is used to estimate an unknown population parameter; an estimate is the result from the actual application of the function to a particular set of data. ...

In some situations, cluster analysis is only appropriate when the clusters are approximately the same size. This can be achieved by combining clusters. If this is not possible, probability proportionate to size sampling is used. In this method, the probability of selecting any cluster varies with the size of the cluster, giving larger clusters a greater probability of selection and smaller clusters a lower probability. However, if clusters are selected with probability proportionate to size, the same number of interviews should be carried out in each sampled cluster so that each unit sampled has the same probability of selection.

Cluster sampling is used to estimate high mortalities in cases such as wars, famines and natural disasters.^[1] For other uses, see War (disambiguation). ... <nowiki>Insert non-formatted text hereBold text</nowiki>A famine is a social and economic crisis that is commonly accompanied by widespread malnutrition, starvation, epidemic and increased mortality. ... Mount Pinatubo eruption, 1991 A natural disaster is the consequence of a natural hazard (e. ...

Advantages

• Can be cheaper than other methods - e.g. fewer travel expenses, administration costs

Disadvantages

• Higher Sampling error, which is difficult to measure

In statistics, when analyzing collected data, the samples observed differ in such things as means and standard deviations from the population from which the sample is taken. ...

References

See also

• Sampling (statistics)
• Multistage sampling
• Simple random sampling