Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Design concurent multinivel cu metode mixte× | Design Secvențial Explicativ cu Metode Mixte× | |
|---|---|---|
| Domeniu | Design de cercetare | Design de cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autorul original≠ | John W. Creswell & Vicki L. Plano Clark; Anthony Onwuegbuzie & colleagues | John W. Creswell & Vicki L. Plano Clark |
| Tip | Mixed methods research design | Mixed methods research design |
| Sursa seminală | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344996 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Denumiri alternative | simultaneous multilevel mixed methods, parallel multilevel mixed methods, multilevel concurrent mixed methods, QUAN+QUAL multilevel design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Înrudite | 6 | 6 |
| Rezumat≠ | Concurrent multilevel mixed methods design collects quantitative and qualitative data simultaneously at two or more levels of a nested social system — for example, students within classrooms within schools — then integrates findings across those levels to produce a layered, comprehensive understanding of the phenomenon. The concurrent timing means both data strands are gathered in the same phase rather than one informing the other sequentially. | 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. |
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