Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Meta-inferència de mètodes mixts concurrents× | Disseny explicatiu seqüencial de mètodes mixts× | |
|---|---|---|
| Camp | Disseny de recerca | Disseny de recerca |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 2003 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autor original≠ | Abbas Tashakkori & Charles Teddlie | John W. Creswell & Vicki L. Plano Clark |
| Tipus | Mixed methods research design | Mixed methods research design |
| Font seminal≠ | Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage. ISBN: 978-0761920731 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Àlies | concurrent meta-inference, simultaneous mixed methods meta-inference, parallel strand meta-inference, QUAN+QUAL meta-inference | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Relacionats | 6 | 6 |
| Resum≠ | Concurrent mixed methods meta-inference is a research design in which quantitative and qualitative data strands are collected simultaneously and then subjected to a formal meta-inferential process — drawing a unified, overarching conclusion that transcends what either strand alone could produce. The concurrent timing means neither strand informs the collection of the other; instead, both strands converge at the analysis-integration stage where meta-inferences are constructed. | 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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