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| Wieloprzypadkowa interpretacyjna analiza fenomenologiczna× | Studium wielu przypadków× | |
|---|---|---|
| Dziedzina | Metody jakościowe | Metody jakościowe |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1996 (IPA); multi-case design consolidated circa 2009 | 1980s–1990s (Yin's first edition 1984; Stake's collective case study concept 1995) |
| Twórca≠ | Jonathan A. Smith (IPA); multi-case extension elaborated by Smith, Flowers & Larkin | Robert K. Yin (systematic replication logic); Robert E. Stake (naturalistic/collective case tradition) |
| Typ≠ | Qualitative interpretive research design | Qualitative research method |
| Źródło pierwotne≠ | Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage. ISBN: 978-1412908344 | Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage. ISBN: 978-1506336169 |
| Inne nazwy | multi-case IPA, multiple-case IPA, cross-case interpretive phenomenological analysis, IPA multiple case design | comparative case study, multi-site case study, collective case study, cross-case analysis |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | Multiple case-based interpretive phenomenological analysis (multi-case IPA) applies the close, idiographic reading of IPA to a set of purposively selected cases, conducting detailed within-case analysis before systematically comparing themes across cases. The approach retains IPA's commitment to understanding individual lived experience in depth while allowing the researcher to identify convergent and divergent patterns across structurally similar situations or participant groups. | 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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