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| Онлайн изследване на отклоняващи се случаи× | Максимално вариативно подбиране× | |
|---|---|---|
| Област | Методология на проучванията | Методология на проучванията |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s (deviant case strategy); online variant ~2000s–2010s | 1985 (Lincoln & Guba); elaborated 1990–2002 (Patton) |
| Създател≠ | Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice | Lincoln & Guba; systematised by Michael Quinn Patton |
| Тип≠ | Purposive qualitative sampling strategy (online variant) | Purposive qualitative sampling strategy |
| Основополагащ източник≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Purposeful Sampling, deviant/extreme case strategy, pp. 231-234] ISBN: 978-0761919711 | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. Chapter 5: Purposeful Sampling. ISBN: 978-0761919711 |
| Други названия | online extreme case sampling, internet-based deviant case sampling, online outlier sampling, web-based atypical case sampling | maximum variation sampling, maximum diversity sampling, MVS, heterogeneous sampling |
| Свързани | 5 | 5 |
| Резюме≠ | Online deviant case sampling is a purposive qualitative sampling strategy in which the researcher deliberately seeks out and recruits participants who represent extreme, unusual, or outlier instances of the phenomenon under study, using online channels such as forums, social media, specialist communities, or digital registries. It inherits the logic of Patton's deviant (extreme) case sampling and applies it in internet-mediated research contexts where rare or hard-to-reach atypical cases can be located more efficiently than through face-to-face methods. | Maximum variation sampling is a purposive qualitative sampling strategy in which the researcher deliberately selects cases that span the widest possible range of variation on dimensions central to the study. The goal is not statistical representation but the identification of common patterns that cut across diverse cases as well as the documentation of the unique ways each context shapes the phenomenon under investigation. |
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