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Lĩnh vựcPhương pháp luận khảo sátPhương pháp luận khảo sát
HọProcess / pipelineProcess / pipeline
Năm ra đời1960s–1980s (developed alongside large-scale survey programs)1953–1965 (formalized in survey sampling literature)
Người khởi xướngLeslie Kish (probability sampling theory); complex survey methodologistsWilliam G. Cochran; Leslie Kish
LoạiProbability sampling designProbability sampling design
Công trình gốcKish, L. (1965). Survey Sampling. John Wiley & Sons. New York. ISBN: 978-0471109495Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Tên gọi kháchierarchical weighted sampling, nested weighted sampling, multilevel probability weighting, weighted hierarchical samplingproportionate stratified sampling, proportional allocation stratified sampling, PSRS, proportionate stratified random sampling
Liên quan66
Tóm tắtMulti-level weighted sampling is a probability-based survey design that draws samples from hierarchically nested populations — such as students within classrooms within schools within districts — and assigns design weights at each level to account for unequal selection probabilities. The resulting weighted data enable unbiased population-level inference despite the complex, non-proportional structure of the sampling frame. It is the backbone of major international assessments such as PISA and TIMSS.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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ScholarGateSo sánh phương pháp: Multi-level weighted sampling · Proportional Stratified Sampling. Truy cập ngày 2026-06-18 từ https://scholargate.app/vi/compare