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| Campionamento a Massima Variazione Adattivo× | Campionamento Intenzionale× | |
|---|---|---|
| Campo | Metodologia delle indagini | Metodologia delle indagini |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (practice codified in qualitative methods literature) | Formalized ~1980–1990 |
| Ideatore≠ | Synthesizes Patton (maximum variation) and Thompson (adaptive sampling) | Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry |
| Tipo≠ | Adaptive purposive qualitative sampling strategy | Non-probability sampling strategy |
| Fonte seminale≠ | Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183] ISBN: 978-0803937796 | Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. ISBN: 978-0803937796 |
| Alias | adaptive purposive maximum variation sampling, iterative maximum variation sampling, adaptive heterogeneous sampling, AMVS | judgmental sampling, selective sampling, criterion-based sampling, purposeful sampling |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Adaptive maximum variation sampling is a purposive qualitative sampling strategy that combines the logic of maximum variation sampling — deliberately selecting cases that differ as widely as possible on key dimensions — with an adaptive, iterative recruitment process. Rather than fixing the full sample in advance, the researcher continuously reviews emerging data to identify which types of cases are underrepresented and recruits new participants to fill those gaps, maximizing heterogeneity throughout data collection. | Purposive sampling is a non-probability strategy in which the researcher deliberately selects participants, documents, or cases that are information-rich with respect to the research question. Rather than drawing units at random, the researcher applies explicit criteria aligned with the study's purpose, maximising the depth and relevance of the data collected. It is the default sampling logic in most qualitative research designs and is also used in mixed-methods and applied evaluative work. |
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