Metodbevispost

Adaptive Cluster Sampling

Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.

Sources recorded, not reviewed

Källpost

Citat kopierade ordagrant från metodens källpost. Ingen verifiering på källnivå härleds från dem.

Taxonomisk metodpost · process-pipeline / survey-methodology

Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. · DOI 10.2307/2289601
Thompson, S. K., & Seber, G. A. F. (1996). Adaptive Sampling. Wiley. · ISBN 978-0471558712

Öppna fullständig metod

Kuraterade påståenden

Påståenden lagrade i bevisloggen, var och en med sin egen bedömning.

Inga kuraterade påståenden ännu

Denna vy hittar inte på en påståendebedömning när loggen saknar en.

Relaterade metoder

Genererade från metodgrafen och visade som maskinföreslagna relationer – inga bevispåståenden härleds.

Taxonomic bucketAdaptive Stratified Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketCluster Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultistage Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSnowball Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyStratified Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSystematic Samplingmachine-suggested · Relational suggestion, not evidence.