Sammenlign metoder

	Maksimal variasjonsutvelgelse	Formålsrettet utvalg	Respondent-Driven Sampling
Fagfelt	Surveymetodikk	Surveymetodikk	Surveymetodikk
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	1985 (Lincoln & Guba); elaborated 1990–2002 (Patton)	Formalized ~1980–1990	1997
Opphavsperson	Lincoln & Guba; systematised by Michael Quinn Patton	Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry	Douglas Heckathorn
Type	Purposive qualitative sampling strategy	Non-probability sampling strategy	Probabilistic chain-referral sampling design
Opprinnelig kilde	Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. Chapter 5: Purposeful Sampling. ISBN: 978-0761919711	Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. ISBN: 978-0803937796	Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.
Alias	maximum variation sampling, maximum diversity sampling, MVS, heterogeneous sampling	judgmental sampling, selective sampling, criterion-based sampling, purposeful sampling	Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme
Relaterte	5	4	3
Sammendrag	Maximum variation sampling is a purposive qualitative sampling strategy in which the researcher deliberately selects cases that span the widest possible range of variation on dimensions central to the study. The goal is not statistical representation but the identification of common patterns that cut across diverse cases as well as the documentation of the unique ways each context shapes the phenomenon under investigation.	Purposive sampling is a non-probability strategy in which the researcher deliberately selects participants, documents, or cases that are information-rich with respect to the research question. Rather than drawing units at random, the researcher applies explicit criteria aligned with the study's purpose, maximising the depth and relevance of the data collected. It is the default sampling logic in most qualitative research designs and is also used in mixed-methods and applied evaluative work.	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.