Methoden vergleichen

	Design-basiertes Multilevel Mixed Methods	Multilevel Modeling
Fachgebiet	Forschungsdesign	Forschungsstatistik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s	1992
Urheber	Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology	Anthony Bryk and Stephen Raudenbush
Typ	Mixed methods research design	Method
Wegweisende Quelle	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.
Aliasnamen	DB-MLMM, multilevel design-based mixed methods, design-based multilevel research, DBR multilevel mixed design	HLM, mixed-effects models, random effects models, MLM
Verwandt	3	3
Zusammenfassung	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.