Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi Jumuishi wa Mbinu Mseto Sambamba× | Muundo wa Mbinu Mchanganyiko wa Maelezo Mfululizo× | |
|---|---|---|
| Nyanja | Muundo wa Utafiti | Muundo wa Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2003 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Mwanzilishi≠ | Abbas Tashakkori & Charles Teddlie | John W. Creswell & Vicki L. Plano Clark |
| Aina | Mixed methods research design | Mixed methods research design |
| Chanzo asilia≠ | 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 |
| Majina mbadala | 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 |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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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