Methoden vergleichen

	Eingebettetes erklärendes sequenzielles Mixed-Methods-Design	Erklärendes sequenzielles Mixed-Methods-Design
Fachgebiet	Forschungsdesign	Forschungsdesign
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2007–2011	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Urheber	John W. Creswell & Vicki L. Plano Clark	John W. Creswell & Vicki L. Plano Clark
Typ	Mixed methods research design	Mixed methods research design
Wegweisende Quelle	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
Aliasnamen	embedded QUAN→QUAL design, nested explanatory sequential design, embedded mixed methods with explanatory sequence, QUAN(qual) explanatory embedded design	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Verwandt	5	6
Zusammenfassung	The embedded explanatory sequential mixed methods design combines two structural logics: the explanatory sequential framework (a dominant quantitative phase followed by a qualitative follow-up) and the embedded design principle (one method nested within the other to serve a supporting role). Quantitative data are collected and analyzed first to identify patterns or outcomes; qualitative data are then gathered — embedded within or alongside the QUAN phase — to explain, interpret, or contextualize those findings. The result is a study in which numerical results drive the inquiry and qualitative voices provide the explanatory depth.	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.