विधियों की तुलना करें

	मात्रात्मक-प्रमुख बहुस्तरीय मिश्रित विधि डिज़ाइन	बहु-चरण मिश्रित विधि डिज़ाइन
क्षेत्र	अनुसंधान अभिकल्प	अनुसंधान अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2003–2010	2007 (first edition of Designing and Conducting Mixed Methods Research)
प्रवर्तक	Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie	John W. Creswell & Vicki L. Plano Clark
प्रकार	Mixed methods research design	Mixed methods research design
मौलिक स्रोत	Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications. ISBN: 978-1412972666	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483substitute
उपनाम	QUAN-dominant multilevel MMR, multilevel mixed methods with quantitative priority, QUAN-priority multilevel design, dominant-status multilevel mixed methods	multiphase design, multiproject mixed methods, programmatic mixed methods, multistage mixed methods
संबंधित	6	6
सारांश	Quantitative-dominant multilevel mixed methods design is a mixed methods approach in which quantitative inquiry carries the primary evidential weight while qualitative data play an auxiliary, illuminating role, and both strands are applied across two or more hierarchically nested levels of analysis — for example, students within classrooms within schools. The design is suited to research questions that require both statistical modeling of nested structures and contextual understanding of how those structures operate.	The multiphase mixed methods design is a sustained research program in which quantitative and qualitative strands are combined across three or more sequential phases — or across multiple related projects — to address a central program objective. Each phase builds on the prior phase's findings, making the design well-suited to long-term evaluation, intervention development, and large-scale program assessment where a single data-collection cycle cannot fully address the complexity of the research problem.