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An example of cluster sampling is area sampling or geographical cluster sampling.Each cluster is a geographical area in an area sampling frame.Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster.
In statistics, multistage sampling is the taking of samples in stages using smaller and smaller sampling units at each stage. [1] Multistage sampling can be a complex form of cluster sampling because it is a type of sampling which involves dividing the population into groups (or clusters). Then, one or more clusters are chosen at random and ...
For this reason, cluster sampling requires a larger sample than SRS to achieve the same level of accuracy – but cost savings from clustering might still make this a cheaper option. Cluster sampling is commonly implemented as multistage sampling. This is a complex form of cluster sampling in which two or more levels of units are embedded one ...
Cluster analysis, a set of techniques for grouping a set of objects based on intrinsic similarities; Cluster sampling, a sampling technique used when "natural" groupings are evident in a statistical population; Cluster graph, in graph theory, a disjoint union of complete graphs; Clusterable graph, in balance theory
Cluster data describes data where many observations per unit are observed. This could be observing many firms in many states or observing students in many classes. In such cases, the correlation structure is simplified, and one does usually make the assumption that data is correlated within a group/cluster, but independent between groups/clusters.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
Point sampling can be based on a two-stage scheme, sampling clusters in the first stage and sampling points in the second stage. Another option is a two-phase scheme of unclustered points: a large first-phase sample is selected. A stratification is conducted only for the first-phase sample and a stratified sample is chosen in the second phase.
Adaptive cluster sampling tracks the selection probabilities for later phases of sampling so that an unbiased estimate of the population mean can be calculated despite oversampling of certain areas. An example application of adaptive cluster sampling is delineating the borders of a plume of contamination.