Compara mètodes

	Disseny seqüencial exploratori mixt centrat en l'avaluació	Disseny exploratori seqüencial de mètodes mixts
Camp	Disseny de recerca	Disseny de recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	2000s–2010s	1990s–2000s (codified by ~2007)
Autor original	John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Jennifer C. Greene & Donna M. Mertens (evaluation framing)	John W. Creswell & Vicki L. Plano Clark
Tipus	Mixed methods research design	Mixed methods research design
Font seminal	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
Àlies	evaluative exploratory sequential MMR, exploratory sequential evaluation design, QUAL→QUAN evaluation mixed methods	QUAL → QUAN design, exploratory sequential design, instrument-development design, theory-building mixed methods
Relacionats	6	6
Resum	The evaluation-focused exploratory sequential mixed methods design combines program evaluation goals with a two-phase sequential structure: qualitative inquiry precedes and informs a quantitative phase. Phase 1 explores stakeholder experiences or program processes through interviews or focus groups; the findings build an instrument or framework used to measure outcomes quantitatively in Phase 2. The approach is used when little is known about a program's mechanisms, and both understanding and generalizing findings to a wider population matter.	The exploratory sequential mixed methods design is a two-phase research framework in which a qualitative phase is conducted first to explore a poorly understood phenomenon, and the findings then inform a subsequent quantitative phase — typically to develop and test a survey instrument, measure a theory, or generalize qualitative insights to a larger population. The qualitative strand guides what is measured; the quantitative strand tests or extends those findings at scale.