विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| अनुक्रमिक गुणात्मक-प्राथमिकता मिश्रित डिज़ाइन× | व्याख्यात्मक अनुक्रमिक मिश्रित विधि डिज़ाइन× | |
|---|---|---|
| क्षेत्र | अनुसंधान अभिकल्प | अनुसंधान अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2000s–2010s | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| प्रवर्तक≠ | Creswell & Plano Clark; Teddlie & Tashakkori | John W. Creswell & Vicki L. Plano Clark |
| प्रकार | Mixed methods research design | Mixed methods research design |
| मौलिक स्रोत | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| उपनाम | QUAL-priority sequential design, qualitative-dominant sequential design, qual-first sequential mixed methods, sequential exploratory qualitative-priority design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | Sequential qualitative-priority mixed design is a two-phase mixed methods approach in which a qualitative strand is conducted first and carries greater weight in the overall study. The quantitative phase follows and serves to extend, test, or generalize the qualitative findings. The QUAL-first, QUAL-dominant logic makes this design well suited to exploratory research where theory or instruments are underdeveloped and must be grounded in participants' own words before being scaled up. | 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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