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| Ma trận Phương pháp Trộn lẫn Đồng thời× | Thiết kế Phương pháp hỗn hợp tuần tự thăm dò× | |
|---|---|---|
| Lĩnh vực | Thiết kế nghiên cứu | Thiết kế nghiên cứu |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2000s–2010s | 1990s–2000s (codified by ~2007) |
| Người khởi xướng≠ | Teddlie & Tashakkori; Creswell & Plano Clark | John W. Creswell & Vicki L. Plano Clark |
| Loại | Mixed methods research design | Mixed methods research design |
| Công trình gốc≠ | Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage. ISBN: 978-0761930129 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Tên gọi khác | concurrent MM matrix, simultaneous mixed methods matrix, parallel mixed methods matrix, concurrent matrix mixed design | QUAL → QUAN design, exploratory sequential design, instrument-development design, theory-building mixed methods |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | The concurrent mixed methods matrix is a mixed methods design in which quantitative and qualitative data strands are collected simultaneously and organized within a structured matrix framework. The matrix maps design dimensions — such as research questions, data sources, priority, and integration points — across rows and columns, making the logical architecture of the study explicit and auditable. Both strands are analyzed independently before being merged through a matrix-guided integration step. | The exploratory sequential mixed methods design is a two-phase research framework in which a qualitative phase is conducted first to explore a poorly understood phenomenon, and the findings then inform a subsequent quantitative phase — typically to develop and test a survey instrument, measure a theory, or generalize qualitative insights to a larger population. The qualitative strand guides what is measured; the quantitative strand tests or extends those findings at scale. |
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