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תחוםמתודולוגיית סקריםמתודולוגיית סקרים
משפחהProcess / pipelineProcess / pipeline
שנת המקור1990s–2000s (practice codified in qualitative methods literature)1961
הוגה השיטהSynthesizes Patton (maximum variation) and Thompson (adaptive sampling)Leo A. Goodman
סוגAdaptive purposive qualitative sampling strategyNon-probability sampling technique
מקור מכונןPatton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183] ISBN: 978-0803937796Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170. DOI ↗
כינוייםadaptive purposive maximum variation sampling, iterative maximum variation sampling, adaptive heterogeneous sampling, AMVSchain-referral sampling, network sampling, respondent-driven sampling, referral sampling
קשורות53
תקצירAdaptive 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.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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ScholarGateהשוואת שיטות: Adaptive Maximum Variation Sampling · Snowball Sampling. אוחזר בתאריך 2026-06-15 מתוך https://scholargate.app/he/compare