مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| ماتریس روشهای ترکیبی با غلبه کیفی× | طرح پژوهش ترکیبی متوالی اکتشافی با غلبه کیفی× | |
|---|---|---|
| حوزه | طراحی پژوهش | طراحی پژوهش |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 2003–2009 | 2003–2007 |
| پدیدآور≠ | Charles Teddlie & Abbas Tashakkori (matrix framework); qualitative-dominant weighting drawn from Jennifer Greene and David Morgan | Creswell & Plano Clark; Morse (priority notation) |
| نوع≠ | Mixed methods research design variant | Mixed methods research design |
| منبع بنیادین≠ | 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 |
| نامهای دیگر | QUAL-dominant MMM, qualitative-priority mixed methods matrix, QUAL-weighted matrix design, qual-dominant typology matrix | QUAL-dominant exploratory sequential design, qual-first exploratory mixed methods, qualitative-priority exploratory sequential MMR, QUAL → quan exploratory design |
| مرتبط≠ | 5 | 6 |
| خلاصه≠ | The qualitative-dominant mixed methods matrix is a design variant in which the researcher selects and positions a specific mixed methods design within a typological matrix — organized by timing (sequential vs. concurrent) and paradigm weighting — while assigning greater priority to the qualitative strand. Quantitative data play a supporting, supplementary role, and the final inferences are grounded primarily in qualitative findings. | This design begins with a substantive qualitative phase (QUAL) that drives the study, followed by a smaller quantitative phase (quan) used to test, refine, or extend qualitative findings to a broader sample. The qualitative strand holds priority in both scope and interpretation; the quantitative strand serves a confirmatory or generalisability function. It is particularly well suited when theory or instrument development must be grounded in participants' own frameworks before statistical testing. |
| ScholarGateمجموعهداده ↗ |
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