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| Evaluativer explorativer sequenzieller Mixed-Methods-Ansatz× | Erklärendes sequenzielles Mixed-Methods-Design× | |
|---|---|---|
| Fachgebiet | Forschungsdesign | Forschungsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Urheber≠ | John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Jennifer C. Greene & Donna M. Mertens (evaluation framing) | John W. Creswell & Vicki L. Plano Clark |
| Typ | Mixed methods research design | Mixed methods research design |
| Wegweisende Quelle | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Aliasnamen≠ | evaluative exploratory sequential MMR, exploratory sequential evaluation design, QUAL→QUAN evaluation mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | The evaluation-focused exploratory sequential mixed methods design combines program evaluation goals with a two-phase sequential structure: qualitative inquiry precedes and informs a quantitative phase. Phase 1 explores stakeholder experiences or program processes through interviews or focus groups; the findings build an instrument or framework used to measure outcomes quantitatively in Phase 2. The approach is used when little is known about a program's mechanisms, and both understanding and generalizing findings to a wider population matter. | The explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation. |
| ScholarGateDatensatz ↗ |
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