Confronta i metodi

	Metodi Misti Multilivello Basati sul Design	Modellazione multilivello
Campo	Disegno della ricerca	Statistica per la ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2000s–2010s	1992
Ideatore	Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology	Anthony Bryk and Stephen Raudenbush
Tipo	Mixed methods research design	Method
Fonte seminale	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.
Alias	DB-MLMM, multilevel design-based mixed methods, design-based multilevel research, DBR multilevel mixed design	HLM, mixed-effects models, random effects models, MLM
Correlati	3	3
Sintesi	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.