Process / pipelineSampling design

Adaptive Cluster Sampling

Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.

Find Topic with PaperMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI: 10.1080/01621459.1990.10474975

Related methods

Referenced by

ScholarGateAdaptive Sampling (Adaptive Cluster Sampling). Retrieved 2026-06-04 from https://scholargate.app/en/survey-methodology/adaptive-sampling