Porównaj metody

	Ilościowo-dominująca metody mieszane skoncentrowane na przypadku	Wyjaśniający sekwencyjny projekt mieszany metod
Dziedzina	Projektowanie badań	Projektowanie badań
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2003–2007	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Twórca	Tashakkori & Teddlie (dominant-status typology); Creswell & Plano Clark (design taxonomy)	John W. Creswell & Vicki L. Plano Clark
Typ	Mixed methods research design	Mixed methods research design
Źródło pierwotne	Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage. ISBN: 978-1412972666	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
Inne nazwy	QUAN-dominant case study MMR, quantitatively weighted case mixed methods, dominant-status case-centered mixed design	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Pokrewne	6	6
Podsumowanie	Quantitative-dominant case-focused mixed methods organizes a study around one or more clearly bounded cases while assigning primary weight and inferential authority to quantitative data. Qualitative data are collected within the same case boundaries and serve an augmenting, explanatory, or contextual role rather than an equal one. The design is ideal when a case (a school, organization, community, or patient cohort) is the unit of analysis and the core research questions require measurable outcomes that qualitative evidence then helps interpret or explain.	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.