方法对比
并排查看您选择的方法;存在差异的行会高亮显示。
| 定量主导的多层混合方法设计× | 解释性顺序混合方法设计× | |
|---|---|---|
| 领域 | 研究设计 | 研究设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2003–2010 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| 提出者≠ | Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie | John W. Creswell & Vicki L. Plano Clark |
| 类型 | Mixed methods research design | Mixed methods research design |
| 开创性文献≠ | Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications. ISBN: 978-1412972666 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| 别名 | QUAN-dominant multilevel MMR, multilevel mixed methods with quantitative priority, QUAN-priority multilevel design, dominant-status multilevel mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| 相关 | 6 | 6 |
| 摘要≠ | Quantitative-dominant multilevel mixed methods design is a mixed methods approach in which quantitative inquiry carries the primary evidential weight while qualitative data play an auxiliary, illuminating role, and both strands are applied across two or more hierarchically nested levels of analysis — for example, students within classrooms within schools. The design is suited to research questions that require both statistical modeling of nested structures and contextual understanding of how those structures operate. | 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. |
| ScholarGate数据集 ↗ |
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