Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ubunifu wa mbinu mchanganyiko wa vitendo unaoendana× | 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≠ | John W. Creswell & Vicki L. Plano Clark; philosophical grounding by R. Burke Johnson & Anthony Onwuegbuzie | 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≠ | concurrent pragmatic design, pragmatic concurrent mixed methods, simultaneous pragmatic mixed methods | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | Concurrent pragmatic mixed methods is a research design that collects quantitative and qualitative data simultaneously within a pragmatic philosophical framework. Rather than privileging either positivism or constructivism, the pragmatic stance selects methods based on what best answers the research question. Both data strands are gathered in parallel, then merged at the interpretation stage to provide a fuller picture than either strand alone could yield. | 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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