Sammenlign metoder

	Kvantitativt-dominerende mixed methods meta-inferens	Kvantitativt-dominant Multilevel Mixed Methods Design
Fagområde	Forskningsdesign	Forskningsdesign
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2003–2007	2003–2010
Ophavsperson	Tashakkori & Teddlie (meta-inference concept); Creswell & Plano Clark (dominance weighting framework)	Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie
Type	Mixed methods integration procedure	Mixed methods research design
Oprindelig kilde	Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage. ISBN: 978-0761920731	Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications. ISBN: 978-1412972666
Aliasser	QUAN-dominant meta-inference, quantitatively weighted meta-inference, QUAN-priority integration inference, quantitative-weighted mixed inference	QUAN-dominant multilevel MMR, multilevel mixed methods with quantitative priority, QUAN-priority multilevel design, dominant-status multilevel mixed methods
Relaterede	6	6
Resumé	Quantitative-dominant mixed methods meta-inference is an integration procedure in which the researcher draws an overarching conclusion by combining inferences from both quantitative and qualitative strands, while explicitly assigning greater evidential weight to the quantitative results. The qualitative strand serves a supporting, elaborating, or contextualizing role rather than an equal voice in the final interpretation.	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.