เปรียบเทียบวิธี

	เมทริกซ์วิธีการแบบผสมผสานที่มุ่งเน้นการประเมิน	เมทริกซ์วิธีการแบบผสม
สาขาวิชา	การออกแบบการวิจัย	การออกแบบการวิจัย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2000s (Teddlie & Tashakkori 2009; Greene 2007)	2003–2010
ผู้ริเริ่ม	Teddlie & Tashakkori (matrix framework); Greene (evaluation-oriented framing)	Tashakkori & Teddlie; Onwuegbuzie & Teddlie
ประเภท	Mixed methods design variant	Research design classification and planning tool
แหล่งต้นตำรับ	Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage. ISBN: 978-0761930129	Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351-383). Sage.
ชื่อเรียกอื่น	evaluation MMM, mixed methods display matrix for evaluation, evaluation-focused methods matrix, program evaluation mixed methods matrix	MMR matrix, mixed-methods design matrix, research design classification matrix, mixed methods typology matrix
ที่เกี่ยวข้อง	5	6
สรุป	The evaluation-oriented mixed methods matrix is a structured planning and display tool applied within program evaluation contexts. It maps evaluation questions against data sources, timing, and method types — quantitative and qualitative — in a grid format, making the integration logic explicit and auditable. Rooted in Greene's value-engaged mixed methods tradition and Teddlie and Tashakkori's matrix framework, it serves evaluators who must justify methodological choices to stakeholders while addressing multiple evaluation purposes simultaneously.	The mixed methods matrix is a systematic framework for classifying, planning, and comparing mixed methods research designs along key dimensions such as timing (concurrent vs. sequential), priority (quantitative- vs. qualitative-dominant), and point of integration. It provides researchers with a structured map to make design decisions explicit, communicate choices transparently, and locate a study within the broader mixed methods typology.