Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Sekvenční smíšený design s kvantitativní prioritou× | Vysvětlující sekvenční smíšený design× | |
|---|---|---|
| Obor | Design výzkumu | Design výzkumu |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2007 (first edition of Designing and Conducting Mixed Methods Research) | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Tvůrce | John W. Creswell & Vicki L. Plano Clark | John W. Creswell & Vicki L. Plano Clark |
| Typ | Mixed methods research design | Mixed methods research design |
| Původní zdroj≠ | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications. ISBN: 978-1483344379 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Další názvy | QUAN-dominant sequential design, quantitative-priority sequential MMR, quan-first sequential mixed methods, quantitative-led sequential design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Příbuzné≠ | 4 | 6 |
| Shrnutí≠ | The sequential quantitative-priority mixed design collects and analyzes quantitative data first, then follows with a qualitative strand to elaborate, explain, or contextualize the quantitative findings. The quantitative component is given greater weight in the overall study, meaning the primary research questions and conclusions are primarily grounded in the quantitative evidence, with the qualitative strand playing a supplementary, explanatory role. | 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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