Сравнение на методи

	Адаптивно снежинково извадково изследване	Извадка, базирана на респонденти (Respondent-Driven Sampling)
Област	Методология на проучванията	Методология на проучванията
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1990s–2000s (as combined approach)	1997
Създател	Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961)	Douglas Heckathorn
Тип	Non-probability / adaptive sampling design	Probabilistic chain-referral sampling design
Основополагащ източник	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.
Други названия	adaptive referral sampling, adaptive chain-referral sampling, dynamic snowball sampling	Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme
Свързани	4	3
Резюме	Adaptive snowball sampling is a hybrid sampling strategy that recruits initial participants (seeds) from a target population and then dynamically adjusts referral chains based on pre-specified criteria — such as population density, diversity, or theoretical saturation. Combining the chain-referral logic of snowball sampling with the responsive decision rules of adaptive sampling, it is particularly suited to studying rare, hidden, or hard-to-reach populations where conventional frames are unavailable.	Respondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.