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| Metodi Misti Sequenziali Pragmatici× | Progettazione esplicativa sequenziale a metodi misti× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2003–2010 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Ideatore≠ | Creswell & Plano Clark; Tashakkori & Teddlie (pragmatic worldview formalization) | John W. Creswell & Vicki L. Plano Clark |
| Tipo | Mixed methods research design | Mixed methods research design |
| Fonte seminale≠ | 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 |
| Alias | sequential pragmatic MMR, pragmatic sequential mixed design, sequential pragma-driven mixed methods, pragmatism-guided sequential mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | Sequential pragmatic mixed methods is a mixed-methods research design in which quantitative and qualitative data strands are collected and analyzed in a defined sequence — one strand following and building on the other — with the entire design anchored in a pragmatic philosophical worldview. Pragmatism foregrounds research usefulness and problem-solving, treating method choice as a practical decision rather than an ideological commitment, and it provides the integrative logic for combining the two strands into actionable insights. | 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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