Sammenlign metoder

	Evalueringsorientert matrise for blandede metoder	Konkurrent triangulering blandet metode-design
Fagfelt	Forskningsdesign	Forskningsdesign
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2000s (Teddlie & Tashakkori 2009; Greene 2007)	2007 (formally named in Creswell & Plano Clark, 1st ed.)
Opphavsperson	Teddlie & Tashakkori (matrix framework); Greene (evaluation-oriented framing)	John W. Creswell & Vicki L. Plano Clark
Type	Mixed methods design variant	Mixed methods research design
Opprinnelig kilde	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	Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage. ISBN: 978-1412975179
Alias	evaluation MMM, mixed methods display matrix for evaluation, evaluation-focused methods matrix, program evaluation mixed methods matrix	convergent parallel design, triangulation design, QUAN+QUAL concurrent design, simultaneous triangulation
Relaterte	5	5
Sammendrag	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 concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, analyzes each strand independently, and then merges the results to assess whether the two data sources corroborate one another. Often called the convergent parallel design, it is one of the foundational configurations in mixed methods research and is chosen specifically when the researcher wants to cross-validate or triangulate findings from two distinct methodological traditions.