ScholarGate
Asistents

Salīdzināt metodes

Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.

Ranga kopu izlase×Klasteru izlase×
NozareIzlases veidošanaAptauju metodoloģija
SaimeProcess / pipelineProcess / pipeline
Izcelsmes gads1952Early-to-mid 20th century; canonical treatment 1953/1977
AutorsGlenn A. McIntyreFormalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
TipsSampling design methodologyProbability sampling design
PirmavotsMcIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390. DOI ↗Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Citi nosaukumiRSScluster random sampling, area sampling, one-stage cluster sampling
Saistītās45
KopsavilkumsRanked Set Sampling (RSS) is a data collection method introduced by G. A. McIntyre in 1952 that improves estimation efficiency when visual ranking of units is easier or cheaper than actual measurement. By deliberately selecting and measuring units that are ranked as most likely to yield desired outcomes, RSS reduces variance compared to simple random sampling while maintaining unbiasedness.Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.
ScholarGateDatu kopa
  1. v1
  2. 3 Avoti
  3. PUBLISHED
  1. v1
  2. 2 Avoti
  3. PUBLISHED

Doties uz meklēšanu Lejupielādēt slaidus

ScholarGateSalīdzināt metodes: Ranked Set Sampling · Cluster Sampling. Izgūts 2026-06-15 no https://scholargate.app/lv/compare