เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การสุ่มตัวอย่างกรณีเบี่ยงเบนออนไลน์× | การเลือกตัวอย่างกรณีที่เบี่ยงเบน× | |
|---|---|---|
| สาขาวิชา | ระเบียบวิธีสำรวจ | ระเบียบวิธีสำรวจ |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1990s–2000s (deviant case strategy); online variant ~2000s–2010s | 1990 |
| ผู้ริเริ่ม≠ | Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice | Michael Quinn Patton |
| ประเภท≠ | Purposive qualitative sampling strategy (online variant) | Purposive qualitative sampling strategy |
| แหล่งต้นตำรับ≠ | 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 |
| ชื่อเรียกอื่น | 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 |
| ที่เกี่ยวข้อง | 5 | 5 |
| สรุป≠ | 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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