Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño de métodos mixtos con prioridad cuantitativa× | Diseño Explicativo Secuencial de Métodos Mixtos× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2003–2009 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autor original≠ | Creswell & Plano Clark; Teddlie & Tashakkori | 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 | QUAN-dominant mixed methods, quantitative-dominant mixed methods, quan-priority design, quantitative-first mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Relacionados | 6 | 6 |
| Resumen≠ | Quantitative-priority mixed methods design is a research approach in which quantitative data and analysis carry the primary explanatory weight, while qualitative data play a supplementary or corroborating role. The researcher collects and analyzes quantitative data first (or concurrently with greater emphasis), then uses qualitative findings to elaborate, explain, or contextualize the statistical results. Priority and sequence together define where integration occurs and how each strand informs the other. | 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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