Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Meta-inferență mixtă dominantă cantitativ× | Design Secvențial Explicativ cu Metode Mixte× | |
|---|---|---|
| Domeniu | Design de cercetare | Design de cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2003–2007 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autorul original≠ | Tashakkori & Teddlie (meta-inference concept); Creswell & Plano Clark (dominance weighting framework) | John W. Creswell & Vicki L. Plano Clark |
| Tip≠ | Mixed methods integration procedure | Mixed methods research design |
| Sursa seminală≠ | 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 |
| Denumiri alternative | QUAN-dominant meta-inference, quantitatively weighted meta-inference, QUAN-priority integration inference, quantitative-weighted mixed inference | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Înrudite | 6 | 6 |
| Rezumat≠ | Quantitative-dominant mixed methods meta-inference is an integration procedure in which the researcher draws an overarching conclusion by combining inferences from both quantitative and qualitative strands, while explicitly assigning greater evidential weight to the quantitative results. The qualitative strand serves a supporting, elaborating, or contextualizing role rather than an equal voice in the final interpretation. | 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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