เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Pilot Weighted Sampling× | การสุ่มตัวอย่างแบบถ่วงน้ำหนัก× | |
|---|---|---|
| สาขาวิชา | ระเบียบวิธีสำรวจ | ระเบียบวิธีสำรวจ |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | Mid-20th century (classical weighted sampling ~1934–1977; pilot study integration formalized in survey practice ~1970s–1980s) | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| ผู้ริเริ่ม≠ | Cochran, W. G.; Neyman, J. | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| ประเภท≠ | Probability sampling with differential selection probabilities in a preliminary study phase | Probability sampling design |
| แหล่งต้นตำรับ≠ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| ชื่อเรียกอื่น | pilot phase weighted sampling, weighted pilot sampling, pilot probability proportional sampling, pilot PPS sampling | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| ที่เกี่ยวข้อง≠ | 3 | 6 |
| สรุป≠ | Pilot weighted sampling applies weighted (unequal-probability) sampling within a small-scale preliminary study to estimate key design parameters — variance components, design effects, and optimal stratum weights — before committing resources to the full survey. By using differential inclusion probabilities in the pilot, researchers obtain more precise parameter estimates for rarer or more variable subgroups while keeping total pilot cost low. The results directly inform the weighting scheme and sample-size allocation for the main survey. | 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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