Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Sampuli ya Kesi za Kawaida Zenye Uzito× | Sampuli ya mgao× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1990s–2000s (as a mixed-methods extension) | 1930s |
| Mwanzilishi≠ | Derived from Patton's typical case sampling (1990) combined with classical survey weighting principles | Developed in market research and opinion polling, notably applied by George Gallup in the 1930s |
| Aina≠ | Purposive sampling with probability weighting | Non-probability sampling design |
| Chanzo asilia≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. pp. 236–238 (typical case sampling). ISBN: 978-0761919711 | Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann. ISBN: 978-0435827496 |
| Majina mbadala≠ | weighted purposive typical sampling, probability-weighted typical case selection, typical case sampling with weighting, weighted representative case sampling | quota-controlled sampling, quota selection, non-probability quota sampling |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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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