Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Вбудований багатофазний змішаний метод дизайну× | Пояснювальний послідовний змішаний дизайн× | |
|---|---|---|
| Галузь | Дизайн дослідження | Дизайн дослідження |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Автор методу≠ | Creswell & Plano Clark (embedded design); Nastasi et al. (multiphase) | John W. Creswell & Vicki L. Plano Clark |
| Тип | Mixed methods research design | Mixed methods research design |
| Основоположне джерело≠ | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications. ISBN: 9781483344379 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Інші назви | embedded multi-phase mixed methods, nested multiphase design, multiphase embedded MMR, embedded phased mixed design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | Embedded multiphase mixed methods is a research design in which a secondary data strand (qualitative or quantitative) is nested within a primary, dominant strand across two or more sequential study phases. Each phase builds on the prior one, while the embedded strand enhances understanding of specific sub-questions that the dominant strand alone cannot answer. This design is suited to complex, longitudinal, or program-evaluation research problems requiring sustained inquiry across stages. | 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. |
| ScholarGateНабір даних ↗ |
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