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| Phương pháp hỗn hợp can thiệp trọng số bằng nhau× | Thiết kế phương pháp hỗn hợp giải thích tuần tự× | |
|---|---|---|
| Lĩnh vực | Thiết kế nghiên cứu | Thiết kế nghiên cứu |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Người khởi xướng≠ | Creswell & Plano Clark (weighting framework); intervention design tradition in mixed methods | John W. Creswell & Vicki L. Plano Clark |
| Loại | Mixed methods research design | Mixed methods research design |
| Công trình gốc≠ | 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 |
| Tên gọi khác | equal-priority intervention MMR, balanced intervention mixed methods, QUAL=QUAN intervention design, equal-status intervention mixed design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | Equal-weight intervention mixed methods is a research design in which both quantitative and qualitative strands are assigned equal priority and are embedded within or alongside an intervention, program, or experiment. The design evaluates not only whether an intervention works (QUAN outcomes) but also how and why it works or fails (QUAL processes), with neither strand treated as secondary. It is particularly suited to program evaluation, clinical trials with process components, and educational or social interventions. | 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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