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| Viktad kvotprovtagning× | Viktad stickprovsdragning× | |
|---|---|---|
| Ämnesområde | Surveymetodik | Surveymetodik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | Mid-to-late 20th century | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| Upphovsperson≠ | Derived from quota sampling (mid-20th century market research) combined with survey weighting theory (Kalton, 1983) | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| Typ≠ | Non-probability sampling with post-collection weight adjustment | Probability sampling design |
| Ursprungskälla≠ | Kalton, G. (1983). Introduction to Survey Sampling. Sage Publications. ISBN: 978-0803921290 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Alias | quota sampling with weighting, weighted quota survey, post-weighted quota sampling, quota sample weighting | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| Närliggande≠ | 5 | 6 |
| Sammanfattning≠ | Weighted quota sampling combines quota sampling — recruiting a set number of respondents matching pre-specified demographic cells — with post-collection statistical weighting that adjusts each respondent's contribution to match known population proportions. The result is a non-probability design with a bias-correction mechanism, widely used in market research, political polling, and applied social surveys when probability sampling is impractical but representativeness remains a goal. | 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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