Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svēršanas un kalibrēšanas aptaujas× | Stratificētā izlase× | |
|---|---|---|
| Nozare | Aptauju metodoloģija | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2010 | 1977 |
| Autors≠ | Sharon Lohr | William G. Cochran |
| Tips≠ | Estimation adjustment procedure | Probability-based survey sampling design |
| Pirmavots≠ | Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7 |
| Citi nosaukumi | Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket) | Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme |
| Saistītās≠ | 3 | 2 |
| Kopsavilkums≠ | Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be. | Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics. |
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