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	Modellazione multilivello	Valutazione dei Programmi
Campo	Statistica per la ricerca	Metodi sul campo
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1992	1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)
Ideatore	Anthony Bryk and Stephen Raudenbush	Michael Scriven; Daniel Stufflebeam; Peter Rossi
Tipo	Method	Applied evaluation methodology
Fonte seminale	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage. ISBN: 978-0761908944
Alias	HLM, mixed-effects models, random effects models, MLM	evaluation research, program assessment, educational evaluation, systematic program evaluation
Correlati	3	3
Sintesi	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.	Program evaluation is a systematic, empirically grounded process of collecting and analyzing information about a program to determine its merit, worth, or significance. Applied across education, public health, social services, and policy, it addresses questions such as whether a program is reaching its target population, whether it is being implemented as designed, and whether it is producing the intended outcomes. It draws on both quantitative and qualitative methods and serves accountability, improvement, or knowledge-generation purposes.