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Online klyngeutvalg×Enkel tilfeldig utvalg på nett×
FagfeltSurveymetodikkSurveymetodikk
FamilieProcess / pipelineProcess / pipeline
OpprinnelsesårLate 1990s–2000s (as internet surveys became prevalent)Late 1990s–2000s (digital adaptation)
OpphavspersonAdapted from cluster sampling (Mahalanobis, Hansen & Hurwitz, 1940s) to online survey contextsAdapted from classical simple random sampling (Neyman, 1934) for web/digital survey contexts; operationalised by survey methodology researchers from the late 1990s onward
TypeProbability sampling techniqueProbability sampling design
Opprinnelig kildeCouper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494. DOI ↗Couper, M. P. (2008). Designing Effective Web Surveys. Cambridge University Press. ISBN: 978-0521700535
Aliasinternet cluster sampling, web cluster sampling, digital cluster samplingweb simple random sampling, internet SRS, digital random sampling, online SRS
Relaterte45
SammendragOnline cluster sampling applies the classic cluster sampling logic to internet-based research: naturally occurring digital groups — such as online communities, email lists, forum memberships, or institutional user registries — serve as clusters, and selected clusters are surveyed in full or partially via web-based instruments. It offers a practical route to probability-based online samples when no complete list of individuals exists but lists of digital groups are accessible.Online simple random sampling applies the logic of classical simple random sampling (SRS) to digital data collection: every member of a defined online population has an equal and independent probability of being selected, and the survey is administered via web platform, email link, or online panel. The approach combines the statistical rigour of probability sampling with the speed and cost advantages of internet-based survey delivery.
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ScholarGateSammenlign metoder: Online cluster sampling · Online simple random sampling. Hentet 2026-06-17 fra https://scholargate.app/no/compare