Comparar métodos

	Diseño de Métodos Mixtos Multinivel Orientado a la Evaluación	Modelado multinivel	Evaluación de Programas
Campo	Diseño de investigación	Estadística para la investigación	Métodos de campo
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	2000s–2010s	1992	1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)
Autor original	Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark (systematization)	Anthony Bryk and Stephen Raudenbush	Michael Scriven; Daniel Stufflebeam; Peter Rossi
Tipo	Mixed methods evaluation design	Method	Applied evaluation methodology
Fuente seminal	Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage. ISBN: 978-1412975551	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	multilevel mixed methods evaluation, hierarchical mixed methods evaluation, MLM mixed methods, nested mixed methods evaluation	HLM, mixed-effects models, random effects models, MLM	evaluation research, program assessment, educational evaluation, systematic program evaluation
Relacionados	2	3	3
Resumen	Evaluation-oriented multilevel mixed methods is a research design that combines quantitative and qualitative data across hierarchically nested levels of an organization or system — such as students within classrooms within schools — to evaluate a program, policy, or intervention. By capturing outcomes, processes, and contextual factors simultaneously at each level, this design produces richer evaluative inferences than either purely statistical multilevel models or single-level qualitative evaluations alone.	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.