Vertaile menetelmiä

	Arviointikeskeinen eksploratiivinen sekventiaalinen monimenetelmäinen tutkimus	Selittävä sekventiaalinen monimenetelmällinen asetelma
Tieteenala	Tutkimusasetelma	Tutkimusasetelma
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2000s–2010s	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Kehittäjä	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
Tyyppi	Mixed methods research design	Mixed methods research design
Alkuperäislähde	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
Rinnakkaisnimet	evaluative exploratory sequential MMR, exploratory sequential evaluation design, QUAL→QUAN evaluation mixed methods	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Liittyvät	6	6
Tiivistelmä	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 explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation.