Comparar métodos

	Design de Métodos Mistos Multinível com Predominância Quantitativa	Desenho Explicativo Sequencial de Métodos Mistos
Área	Delineamento de pesquisa	Delineamento de pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	2003–2010	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Autor original	Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie	John W. Creswell & Vicki L. Plano Clark
Tipo	Mixed methods research design	Mixed methods research design
Fonte seminal	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: 978-1483344379
Outros nomes	QUAN-dominant multilevel MMR, multilevel mixed methods with quantitative priority, QUAN-priority multilevel design, dominant-status multilevel mixed methods	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Relacionados	6	6
Resumo	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 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.