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분야조사방법론조사방법론
계열Process / pipelineProcess / pipeline
기원 연도1934 (Neyman's stratified sampling theory); field applications throughout 20th century1953–1965 (formalized in survey sampling literature)
창시자Jerzy Neyman (stratified sampling theory); applied broadly in field survey practiceWilliam G. Cochran; Leslie Kish
유형Probability sampling designProbability sampling design
원전Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
별칭field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratificationproportionate stratified sampling, proportional allocation stratified sampling, PSRS, proportionate stratified random sampling
관련66
요약Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set.Proportional stratified sampling divides the target population into non-overlapping strata (subgroups defined by a key characteristic such as age band, region, or gender) and then draws a simple random sample from each stratum so that each stratum's share of the total sample matches its share of the total population. Because each subgroup is represented in exact proportion to its population weight, the resulting sample mirrors the population structure closely without requiring post-hoc weighting adjustments.
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ScholarGate방법 비교: Field-based Stratified Sampling · Proportional Stratified Sampling. 2026-06-18에 다음에서 검색함: https://scholargate.app/ko/compare