Comparer des méthodes

	Matrice de méthodes mixtes concomitantes	Conception multiphasique à méthodes mixtes
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s	2007 (first edition of Designing and Conducting Mixed Methods Research)
Auteur d'origine	Teddlie & Tashakkori; Creswell & Plano Clark	John W. Creswell & Vicki L. Plano Clark
Type	Mixed methods research design	Mixed methods research design
Source fondatrice	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. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483substitute
Alias	concurrent MM matrix, simultaneous mixed methods matrix, parallel mixed methods matrix, concurrent matrix mixed design	multiphase design, multiproject mixed methods, programmatic mixed methods, multistage mixed methods
Apparentées	6	6
Résumé	The concurrent mixed methods matrix is a mixed methods design in which quantitative and qualitative data strands are collected simultaneously and organized within a structured matrix framework. The matrix maps design dimensions — such as research questions, data sources, priority, and integration points — across rows and columns, making the logical architecture of the study explicit and auditable. Both strands are analyzed independently before being merged through a matrix-guided integration step.	The multiphase mixed methods design is a sustained research program in which quantitative and qualitative strands are combined across three or more sequential phases — or across multiple related projects — to address a central program objective. Each phase builds on the prior phase's findings, making the design well-suited to long-term evaluation, intervention development, and large-scale program assessment where a single data-collection cycle cannot fully address the complexity of the research problem.