Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uteuzi wa Tofauti Upeo wa Ngazi Nyingi× | Uchaguzi Wenye Kusudi wa Ngazi Nyingi× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1990s–2000s | 1980s–1990s |
| Mwanzilishi≠ | Synthesized from Patton's maximum variation sampling (1990) and multi-level survey design traditions | Derived from Patton's purposive sampling framework; formalized in multi-site qualitative and mixed-methods research |
| Aina≠ | Purposive qualitative/mixed-methods sampling design | Non-probability sampling strategy |
| Chanzo asilia≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Maximum variation sampling and purposeful sampling strategies] ISBN: 978-0761919711 | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. ISBN: 978-0761919711 |
| Majina mbadala | hierarchical maximum variation sampling, nested maximum diversity sampling, multi-tier purposive variation sampling, MLMVS | hierarchical purposive sampling, nested purposive sampling, multi-tier purposive sampling, multi-site purposive sampling |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Multi-level maximum variation sampling is a purposive strategy that deliberately selects cases at two or more nested organizational levels — such as schools within districts, or patients within clinics — while maximizing heterogeneity on key dimensions at each level. The aim is to capture the full range of variation within a hierarchically structured population so that patterns common across diverse contexts can be identified and context-specific differences can be documented with credibility. | Multi-level purposive sampling applies purposive selection criteria at two or more nested levels of a research hierarchy — for instance, first selecting sites or organizations, then selecting participants within each site. This layered approach allows researchers to align the theoretical logic of purposive sampling with the real-world structure of complex, hierarchical populations, making it especially valuable in multi-site qualitative studies and mixed-methods research. |
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