قارن الطرق

	تصميم المنهج المختلط المدمج المرتكز على الحالة	التصميم المختلط المدمج ذو الأولوية النوعية
المجال	تصميم البحث	تصميم البحث
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2000s (formalized ~2007-2011)	2007 (first edition of Designing and Conducting Mixed Methods Research)
صاحب الطريقة	Creswell & Plano Clark (embedded design); Yin (case-study framework)	Creswell & Plano Clark
النوع	Mixed methods research design	Mixed methods research design
المصدر التأسيسي	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 Publications. ISBN: 978-1483358468
الأسماء البديلة	embedded case-study mixed methods, case-centered embedded MMR, nested case mixed methods, embedded within-case mixed design	qual-dominant embedded design, qualitative-primary embedded MMR, embedded QUAL+quan design, nested qualitative-priority design
ذات صلة	6	5
الملخص	Embedded case-focused mixed methods design combines a case-study unit of analysis with an embedded mixed methods structure, nesting one smaller data strand — typically qualitative — within a dominant primary strand — typically quantitative — all organized around one or more bounded cases. This design enables researchers to answer within-case questions at multiple levels, capturing both statistical patterns and rich contextual meaning for a specific case or set of cases.	The embedded qualitative-priority mixed design nests a secondary quantitative strand within a dominant qualitative inquiry. The qualitative strand drives the research logic, framing the questions, guiding data collection, and anchoring interpretation, while the quantitative component plays a supporting role — typically measuring outcomes, tracking context variables, or confirming patterns emerging from the qualitative core. The result is a rich, theoretically grounded account that remains rooted in participants' meanings while gaining empirical precision where needed.