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Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Mostreig de casos anòmals en línia× | Campionament de casos desviants× | |
|---|---|---|
| Camp | Metodologia d'enquestes | Metodologia d'enquestes |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1990s–2000s (deviant case strategy); online variant ~2000s–2010s | 1990 |
| Autor original≠ | Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice | Michael Quinn Patton |
| Tipus≠ | Purposive qualitative sampling strategy (online variant) | Purposive qualitative sampling strategy |
| Font seminal≠ | 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 Publications. ISBN: 978-0761919711 |
| Àlies | online extreme case sampling, internet-based deviant case sampling, online outlier sampling, web-based atypical case sampling | extreme case sampling, outlier sampling, negative case sampling, deviant-case selection |
| Relacionats | 5 | 5 |
| Resum≠ | 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. | 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. |
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