方法对比
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| 多层加权抽样× | 加权抽样× | |
|---|---|---|
| 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) | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| 提出者≠ | Leslie Kish (probability sampling theory); complex survey methodologists | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| 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 | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| 相关 | 6 | 6 |
| 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. | Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient. |
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