Vertaile menetelmiä

	Yhdenvertaisen painotuksen interventiotutkimuksen yhdistelmätutkimus	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	Creswell & Plano Clark (weighting framework); intervention design tradition in mixed methods	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 Publications. ISBN: 978-1483344379	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
Rinnakkaisnimet	equal-priority intervention MMR, balanced intervention mixed methods, QUAL=QUAN intervention design, equal-status intervention mixed design	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Liittyvät	6	6
Tiivistelmä	Equal-weight intervention mixed methods is a research design in which both quantitative and qualitative strands are assigned equal priority and are embedded within or alongside an intervention, program, or experiment. The design evaluates not only whether an intervention works (QUAN outcomes) but also how and why it works or fails (QUAL processes), with neither strand treated as secondary. It is particularly suited to program evaluation, clinical trials with process components, and educational or social interventions.	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.