Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vícestupňový smíšený design× | Vysvětlující sekvenční smíšený design× | |
|---|---|---|
| Obor | Design výzkumu | Design výzkumu |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | Late 1990s–2000s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Tvůrce≠ | Bonnie Nastasi, John Hitchcock, and collaborators; systematized by Creswell & 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: 978-1483357829 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Další názvy | multilevel MMR, nested mixed methods, hierarchical mixed methods design, cross-level mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | Multilevel mixed methods design is a research approach that collects and integrates both quantitative and qualitative data at two or more distinct levels of a social or organizational hierarchy — for example, individuals nested within classrooms, classrooms within schools, or patients within healthcare teams. By pairing quantitative measurement of outcomes at one level with qualitative exploration of meaning at another, researchers gain a richer, more complete picture than either strand alone could provide. | 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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