Methoden vergleichen

	Adaptive Purposive Sampling	Adaptives Cluster-Sampling
Fachgebiet	Umfragemethodik	Umfragemethodik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–1990s	1990
Urheber	Rooted in Patton's purposive sampling typology; adaptive dimension from iterative qualitative inquiry traditions	Steven K. Thompson
Typ	Qualitative sampling strategy	Probability-based adaptive sampling design
Wegweisende Quelle	Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications. ISBN: 978-0761919711	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
Aliasnamen	iterative purposive sampling, emergent purposive sampling, adaptive qualitative sampling, dynamic purposive sampling	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling
Verwandt	5	6
Zusammenfassung	Adaptive purposive sampling is a qualitative strategy in which the researcher begins with explicitly stated, theory-driven selection criteria and then deliberately revises those criteria as data collection proceeds and new understanding emerges. Unlike fixed purposive sampling — where criteria are locked in before fieldwork — the adaptive variant treats the sampling frame as a working hypothesis that is refined in response to early findings, enabling the study to follow the evidence into unexpected but analytically important directions.	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.