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| Painotettu tyypillisen tapauksen otanta× | Painotettu otanta× | |
|---|---|---|
| Tieteenala | Kyselytutkimuksen metodologia | Kyselytutkimuksen metodologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1990s–2000s (as a mixed-methods extension) | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| Kehittäjä≠ | Derived from Patton's typical case sampling (1990) combined with classical survey weighting principles | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| Tyyppi≠ | Purposive sampling with probability weighting | Probability sampling design |
| Alkuperäislähde≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. pp. 236–238 (typical case sampling). ISBN: 978-0761919711 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Rinnakkaisnimet | weighted purposive typical sampling, probability-weighted typical case selection, typical case sampling with weighting, weighted representative case sampling | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | Weighted typical case sampling combines the purposive logic of typical case selection — choosing cases that represent the modal, average, or most common profile of a population — with post-selection probability weighting. The result is a sample that is both substantively representative (cases reflect the norm) and statistically corrected for differential selection probabilities or population structure. It is used in mixed-methods and survey research where depth of typical examples matters alongside inferential accuracy. | 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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