Salīdzināt metodes

	Iegultā skaidrojošā secīgā jauktās metodes dizains	Daudzposmu jauktās metodes dizains
Nozare	Pētījuma dizains	Pētījuma dizains
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2007–2011	2007 (first edition of Designing and Conducting Mixed Methods Research)
Autors	John W. Creswell & Vicki L. Plano Clark	John W. Creswell & Vicki L. Plano Clark
Tips	Mixed methods research design	Mixed methods research design
Pirmavots	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: 978-1483substitute
Citi nosaukumi	embedded QUAN→QUAL design, nested explanatory sequential design, embedded mixed methods with explanatory sequence, QUAN(qual) explanatory embedded design	multiphase design, multiproject mixed methods, programmatic mixed methods, multistage mixed methods
Saistītās	5	6
Kopsavilkums	The embedded explanatory sequential mixed methods design combines two structural logics: the explanatory sequential framework (a dominant quantitative phase followed by a qualitative follow-up) and the embedded design principle (one method nested within the other to serve a supporting role). Quantitative data are collected and analyzed first to identify patterns or outcomes; qualitative data are then gathered — embedded within or alongside the QUAN phase — to explain, interpret, or contextualize those findings. The result is a study in which numerical results drive the inquiry and qualitative voices provide the explanatory depth.	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.