Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño de métodos mixtos anidados centrados en el caso× | Diseño Explicativo Secuencial de Métodos Mixtos× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2000s (formalized ~2007-2011) | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autor original≠ | Creswell & Plano Clark (embedded design); Yin (case-study framework) | John W. Creswell & Vicki L. Plano Clark |
| Tipo | Mixed methods research design | Mixed methods research design |
| Fuente seminal | 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 |
| Alias | embedded case-study mixed methods, case-centered embedded MMR, nested case mixed methods, embedded within-case mixed design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Relacionados | 6 | 6 |
| Resumen≠ | Embedded case-focused mixed methods design combines a case-study unit of analysis with an embedded mixed methods structure, nesting one smaller data strand — typically qualitative — within a dominant primary strand — typically quantitative — all organized around one or more bounded cases. This design enables researchers to answer within-case questions at multiple levels, capturing both statistical patterns and rich contextual meaning for a specific case or set of cases. | 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. |
| ScholarGateConjunto de datos ↗ |
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