Methoden vergleichen

	Adaptive Maximum Variation Sampling	Adaptives Cluster-Sampling
Fachgebiet	Umfragemethodik	Umfragemethodik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s (practice codified in qualitative methods literature)	1990
Urheber	Synthesizes Patton (maximum variation) and Thompson (adaptive sampling)	Steven K. Thompson
Typ	Adaptive purposive qualitative sampling strategy	Probability-based adaptive sampling design
Wegweisende Quelle	Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183] ISBN: 978-0803937796	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
Aliasnamen	adaptive purposive maximum variation sampling, iterative maximum variation sampling, adaptive heterogeneous sampling, AMVS	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling
Verwandt	5	6
Zusammenfassung	Adaptive maximum variation sampling is a purposive qualitative sampling strategy that combines the logic of maximum variation sampling — deliberately selecting cases that differ as widely as possible on key dimensions — with an adaptive, iterative recruitment process. Rather than fixing the full sample in advance, the researcher continuously reviews emerging data to identify which types of cases are underrepresented and recruits new participants to fill those gaps, maximizing heterogeneity throughout data collection.	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.