Módszerek összehasonlítása
Tekintse át a kiválasztott módszereket egymás mellett; az eltérő sorok kiemelve jelennek meg.
| Tervezettség-alapú több-szintű vegyes módszertani megközelítés× | Multilevel Modellezés× | |
|---|---|---|
| Tudományterület≠ | Kutatástervezés | Kutatási statisztika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 2000s–2010s | 1992 |
| Megalkotó≠ | Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology | Anthony Bryk and Stephen Raudenbush |
| Típus≠ | Mixed methods research design | Method |
| Alapmű≠ | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications. ISBN: 978-1483344379 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Alternatív nevek | DB-MLMM, multilevel design-based mixed methods, design-based multilevel research, DBR multilevel mixed design | HLM, mixed-effects models, random effects models, MLM |
| Kapcsolódó | 3 | 3 |
| Összefoglaló≠ | Design-based multilevel mixed methods combines the iterative, context-sensitive logic of design-based research (DBR) with the analytical power of multilevel data structures and the explanatory depth of mixed methods research. It is used predominantly in educational and organizational research where participants are nested within settings (e.g., students within classrooms within schools) and where a designed intervention must be tested, refined, and understood at multiple organizational levels simultaneously. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
| ScholarGateAdatkészlet ↗ |
|
|