Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño de Métodos Mixtos de Triangulación Concurrente× | Diseño Explicativo Secuencial de Métodos Mixtos× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2007 (formally named in Creswell & Plano Clark, 1st ed.) | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autor original | John W. Creswell & Vicki L. Plano Clark | 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. (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 |
| Alias | convergent parallel design, triangulation design, QUAN+QUAL concurrent design, simultaneous triangulation | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | The concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, analyzes each strand independently, and then merges the results to assess whether the two data sources corroborate one another. Often called the convergent parallel design, it is one of the foundational configurations in mixed methods research and is chosen specifically when the researcher wants to cross-validate or triangulate findings from two distinct methodological traditions. | 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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