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| Adaptív Hólabda Mintavétel× | Hólabda-módszer× | |
|---|---|---|
| Tudományterület | Kérdőíves felmérések módszertana | Kérdőíves felmérések módszertana |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1990s–2000s (as combined approach) | 1961 |
| Megalkotó≠ | Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961) | Leo A. Goodman |
| Típus≠ | Non-probability / adaptive sampling design | Non-probability sampling technique |
| Alapmű≠ | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ | Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170. DOI ↗ |
| Alternatív nevek≠ | adaptive referral sampling, adaptive chain-referral sampling, dynamic snowball sampling | chain-referral sampling, network sampling, respondent-driven sampling, referral sampling |
| Kapcsolódó≠ | 4 | 3 |
| Összefoglaló≠ | 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. | Snowball sampling is a non-probability recruitment technique in which initial participants (seeds) refer the researcher to others who meet the study criteria, and those referrals in turn refer further participants. The sample grows incrementally — like a rolling snowball — until the required size or theoretical saturation is reached. It is the method of choice when a target population has no accessible sampling frame, such as undocumented migrants, illicit drug users, survivors of stigmatised experiences, or members of closed professional networks. |
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