Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Równoległe metody mieszane skoncentrowane na przypadku× | Wyjaśniający sekwencyjny projekt mieszany metod× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2000s–2010s (formalized in Creswell & Plano Clark 2011, 2018) | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Twórca≠ | Creswell & Plano Clark (mixed methods typology); Yin (case study methods) | John W. Creswell & Vicki L. Plano Clark |
| Typ | Mixed methods research design | Mixed methods research design |
| Źródło pierwotne | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. link ↗ | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Inne nazwy | concurrent case study mixed methods, parallel case-focused mixed design, simultaneous case mixed methods, case-embedded concurrent mixed design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Concurrent case-focused mixed methods is a research design in which quantitative and qualitative data are collected simultaneously — rather than in sequence — and both strands are anchored within one or more bounded cases (e.g., a school, a program, a community, or an organisation). The two data strands are analyzed separately, then merged or compared to produce a fuller, case-grounded understanding than either strand could yield alone. | 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. |
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