Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gelijkwaardig Multilevel Mixed-Methods Design× | Explanatory Sequential Mixed Methods Design× | |
|---|---|---|
| Vakgebied | Onderzoeksontwerp | Onderzoeksontwerp |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Grondlegger≠ | Tashakkori & Teddlie; Creswell & Plano Clark | John W. Creswell & Vicki L. Plano Clark |
| Type | Mixed methods research design | Mixed methods research design |
| Oorspronkelijke bron≠ | Creswell, J. W., & Plano Clark, V. L. (2017). 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 |
| Aliassen | QUAN+QUAL multilevel design, equal-status multilevel mixed methods, balanced multilevel mixed methods, equal-priority multilevel mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Verwant | 6 | 6 |
| Samenvatting≠ | Equal-weight multilevel mixed methods is a mixed methods design in which quantitative and qualitative data strands are collected at two or more distinct levels of a social system — such as students, classrooms, and schools — and both strands carry equal analytic priority. The QUAN+QUAL notation (where '+' signals equal weight) is applied across each level, and integration occurs both within and between levels to build a comprehensive, multi-perspectival understanding. | 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. |
| ScholarGateGegevensset ↗ |
|
|