Methoden vergleichen
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| Eingebettetes quantitativ-priorisiertes Mixed-Design× | Erklärendes sequenzielles Mixed-Methods-Design× | |
|---|---|---|
| Fachgebiet | Forschungsdesign | Forschungsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2003–2011 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Urheber≠ | Creswell & Plano Clark (embedded structure); Morse & Niehaus (priority notation) | 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. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage. ISBN: 978-1412975179 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Aliasnamen | QUAN+qual embedded design, quantitative-dominant embedded mixed methods, embedded QUAN design, embedded quantitative-priority design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | The embedded quantitative-priority mixed design is a mixed methods research structure in which a dominant quantitative study (survey, experiment, or longitudinal assessment) provides the primary basis for conclusions, while a qualitative component is embedded within that quantitative framework to address a question the numbers alone cannot answer. Priority and resources lie with the quantitative strand; the qualitative strand enriches, contextualizes, or explains a specific aspect of the larger quantitative investigation. | 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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