Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Iegultās jaukto metožu metainference× | Diferencētā secīgā jauktās metodes dizains× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2003–2007 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autors≠ | Abbas Tashakkori & Charles Teddlie (meta-inference concept); John W. Creswell & Vicki L. Plano Clark (embedded design framework) | John W. Creswell & Vicki L. Plano Clark |
| Tips≠ | Mixed methods inference procedure | Mixed methods research design |
| Pirmavots≠ | 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 |
| Citi nosaukumi | embedded MMR meta-inference, meta-inference in embedded design, integrated meta-inference (embedded), EMMD meta-inference | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Embedded mixed methods meta-inference is the process of drawing a single, overarching conclusion by integrating the inferences from a dominant (primary) strand and an embedded (secondary) strand within an embedded mixed methods design. The embedded strand — typically qualitative nested inside a quantitative study, or vice versa — answers a supplemental question, and meta-inference synthesises both strands into one coherent interpretive claim that neither strand could produce alone. | 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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