Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Proporcionální výběr dostupných jednotek× | Kvótní výběr× | |
|---|---|---|
| Obor | Metodologie dotazníkových šetření | Metodologie dotazníkových šetření |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | Mid-20th century onward | 1930s |
| Tvůrce≠ | Developed within mainstream sampling methodology; no single originator | Developed in market research and opinion polling, notably applied by George Gallup in the 1930s |
| Typ≠ | Non-probability sampling with proportional allocation constraint | Non-probability sampling design |
| Původní zdroj≠ | Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215–217. link ↗ | Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann. ISBN: 978-0435827496 |
| Další názvy≠ | quota-constrained convenience sampling, representative convenience sampling, proportionate accidental sampling, PCS | quota-controlled sampling, quota selection, non-probability quota sampling |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | Proportional convenience sampling is a non-probability technique that recruits participants through convenience while constraining each subgroup's share in the final sample to match its known proportion in the target population. It trades pure random selection for feasibility, but partially compensates by ensuring the sample's compositional profile mirrors the population on one or more key variables such as gender, age group, or academic year. | Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference. |
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