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Cluster sampling. A group of twelve people are divided into pairs, and two pairs are then selected at random. In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research.
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 or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical ...
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).
Cluster analysis, a fundamental task in data mining and machine learning, involves grouping a set of data points into clusters based on their similarity. k -means clustering is a popular algorithm used for partitioning data into k clusters, where each cluster is represented by its centroid.
Clustered standard errors assume that is block-diagonal according to the clusters in the sample, with unrestricted values in each block but zeros elsewhere. In this case, one can define X c {\displaystyle X_{c}} and Ω c {\displaystyle \Omega _{c}} as the within-block analogues of X {\displaystyle X} and Ω {\displaystyle \Omega } and derive ...
Probability-proportional-to-size sampling. In survey methodology, probability-proportional-to-size (pps) sampling is a sampling process where each element of the population (of size N) has some (independent) chance to be selected to the sample when performing one draw. This is proportional to some known quantity so that . [1]: 97 [2]
For example, in cluster sampling we can use a two stage sampling in which we sample each cluster (which may be of different sizes) with equal probability, and then sample from each cluster at the second stage using SRS with a fixed proportion (e.g. sample half of the cluster, the whole cluster, etc.).