Сравнение методов

	Встроенный объяснительный последовательный смешанный метод	Дизайн смешанных методов с одновременной триангуляцией
Область	Дизайн исследования	Дизайн исследования
Семейство	Process / pipeline	Process / pipeline
Год появления	2007–2011	2007 (formally named in Creswell & Plano Clark, 1st ed.)
Автор метода	John W. Creswell & Vicki L. Plano Clark	John W. Creswell & Vicki L. Plano Clark
Тип	Mixed methods research design	Mixed methods research design
Основополагающий источник	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. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage. ISBN: 978-1412975179
Другие названия	embedded QUAN→QUAL design, nested explanatory sequential design, embedded mixed methods with explanatory sequence, QUAN(qual) explanatory embedded design	convergent parallel design, triangulation design, QUAN+QUAL concurrent design, simultaneous triangulation
Связанные	5	5
Сводка	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 concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, analyzes each strand independently, and then merges the results to assess whether the two data sources corroborate one another. Often called the convergent parallel design, it is one of the foundational configurations in mixed methods research and is chosen specifically when the researcher wants to cross-validate or triangulate findings from two distinct methodological traditions.