विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| एम्बेडेड मिक्स्ड मेथड्स मेटा-इन्फ्रेंस× | व्याख्यात्मक अनुक्रमिक मिश्रित विधि डिज़ाइन× | |
|---|---|---|
| क्षेत्र | अनुसंधान अभिकल्प | अनुसंधान अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2003–2007 | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| प्रवर्तक≠ | Abbas Tashakkori & Charles Teddlie (meta-inference concept); John W. Creswell & Vicki L. Plano Clark (embedded design framework) | John W. Creswell & Vicki L. Plano Clark |
| प्रकार≠ | Mixed methods inference procedure | Mixed methods research design |
| मौलिक स्रोत≠ | Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage. ISBN: 978-0761920731 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| उपनाम | embedded MMR meta-inference, meta-inference in embedded design, integrated meta-inference (embedded), EMMD meta-inference | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| संबंधित | 6 | 6 |
| सारांश≠ | Embedded mixed methods meta-inference is the process of drawing a single, overarching conclusion by integrating the inferences from a dominant (primary) strand and an embedded (secondary) strand within an embedded mixed methods design. The embedded strand — typically qualitative nested inside a quantitative study, or vice versa — answers a supplemental question, and meta-inference synthesises both strands into one coherent interpretive claim that neither strand could produce alone. | 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. |
| ScholarGateडेटासेट ↗ |
|
|