Linganisha mbinu

	Muundo wa Mbinu Mchanganyiko Mfuatano wa Kueleza kwa Kushirikisha	Muundo wa Mbinu Mchanganyiko wa Maelezo Mfululizo
Nyanja	Muundo wa Utafiti	Muundo wa Utafiti
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2000s-2010s	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Mwanzilishi	Creswell & Plano Clark (explanatory sequential structure); Mertens (transformative/participatory lens)	John W. Creswell & Vicki L. Plano Clark
Aina	Mixed methods research design	Mixed methods research design
Chanzo asilia	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
Majina mbadala	participatory QUAN-to-QUAL design, community-based explanatory sequential design, transformative explanatory sequential mixed methods, participatory two-phase sequential design	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Zinazohusiana	5	6
Muhtasari	The participatory explanatory sequential mixed methods design combines the two-phase QUAN-to-QUAL structure of the explanatory sequential design with a participatory or transformative worldview. Community members and stakeholders are involved as collaborators — not merely subjects — across all stages, from formulating research questions to interpreting results. Quantitative data are collected and analyzed first; findings that need deeper explanation then drive a second, qualitative phase conducted with and by the community.	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.