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| Nghiên cứu tiểu sử dựa trên nhiều trường hợp× | Thiết kế nghiên cứu trường hợp đa dạng× | |
|---|---|---|
| Lĩnh vực | Định tính | Định tính |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1990s–2000s (cross-case biographical designs consolidated in qualitative methodology literature) | 1980s–1990s (Yin's first edition 1984; Stake's collective case study concept 1995) |
| Người khởi xướng≠ | Synthesised from Robert K. Yin (multiple case logic) and biographical research traditions (Roberts, Chamberlayne, Bornat) | Robert K. Yin (systematic replication logic); Robert E. Stake (naturalistic/collective case tradition) |
| Loại≠ | Qualitative research design | Qualitative research method |
| Công trình gốc | Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage. ISBN: 978-1506336169 | Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage. ISBN: 978-1506336169 |
| Tên gọi khác | multi-case biographical study, cross-case biography, comparative biographical case study, multiple biographical case analysis | comparative case study, multi-site case study, collective case study, cross-case analysis |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | Multiple case-based biographical research combines the cross-case replication logic of multiple case study design with the in-depth life-history orientation of biographical research. Each individual biography is treated as a bounded case, examined first on its own terms, and then analysed comparatively across cases to identify patterns, contrasts, and transferable insights about how lives are shaped by social, historical, and structural forces. | Multiple-case study design investigates two or more bounded real-world cases using the same research protocol, then compares findings across cases to identify patterns, contrasts, and explanatory insights that a single case could not produce. Developed primarily through Robert Yin's replication logic and Robert Stake's collective case tradition, the approach is particularly powerful when a researcher needs to determine whether a phenomenon occurs under varied conditions or to test an emerging theoretical explanation against rival contexts. |
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