Comparar métodos

	Modelado multinivel	Evaluación de Programas
Campo	Estadística para la investigación	Métodos de campo
Familia	Process / pipeline	Process / pipeline
Año de origen	1992	1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)
Autor original	Anthony Bryk and Stephen Raudenbush	Michael Scriven; Daniel Stufflebeam; Peter Rossi
Tipo	Method	Applied evaluation methodology
Fuente seminal	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
Relacionados	3	3
Resumen	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.