Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полевая выборка с максимальной вариативностью× | Отбор по отклоняющимся случаям× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s | 1990 |
| Автор метода≠ | Michael Quinn Patton (maximum variation sampling); adapted for field research contexts | Michael Quinn Patton |
| Тип≠ | Purposive qualitative/mixed-methods sampling strategy | Purposive qualitative sampling strategy |
| Основополагающий источник≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Maximum variation sampling discussed in Chapter 5] ISBN: 978-0761919711 | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications. ISBN: 978-0761919711 |
| Другие названия | field MVS, field-based purposeful maximum variation, maximum heterogeneity field sampling, diverse case field sampling | extreme case sampling, outlier sampling, negative case sampling, deviant-case selection |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Field-based maximum variation sampling is a purposive strategy in which a researcher deliberately selects field sites, ecological plots, communities, or observational units that span the widest possible range of relevant characteristics. By maximising heterogeneity among selected units, the approach ensures that both common patterns shared across diverse conditions and unique features specific to particular contexts are documented, making findings robust across a broad spectrum of real-world variation. | Deviant case sampling is a purposive qualitative sampling strategy in which the researcher intentionally selects cases that are unusual, exceptional, or markedly different from the norm — outliers, extreme successes, or conspicuous failures. The goal is not statistical representation but deep learning from cases that illuminate the boundaries of a phenomenon, challenge prevailing assumptions, or reveal processes that typical cases obscure. |
| ScholarGateНабор данных ↗ |
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