方法对比

	多层加权抽样	系统抽样
领域	调查方法论	调查方法论
方法族	Process / pipeline	Process / pipeline
起源年份	1960s–1980s (developed alongside large-scale survey programs)	Mid-20th century (Cochran 1953; Kish 1965)
提出者	Leslie Kish (probability sampling theory); complex survey methodologists	William G. Cochran; formalized in survey sampling theory
类型	Probability sampling design	Probability sampling design
开创性文献	Kish, L. (1965). Survey Sampling. John Wiley & Sons. New York. ISBN: 978-0471109495	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
别名	hierarchical weighted sampling, nested weighted sampling, multilevel probability weighting, weighted hierarchical sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
相关	6	5
摘要	Multi-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.	Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.