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Viacúrovňové modelovanie×Hodnotenie programov×
OdborŠtatistika vo výskumeTerénne metódy
RodinaProcess / pipelineProcess / pipeline
Rok vzniku19921960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)
TvorcaAnthony Bryk and Stephen RaudenbushMichael Scriven; Daniel Stufflebeam; Peter Rossi
TypMethodApplied evaluation methodology
Pôvodný zdrojBryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage. ISBN: 978-0761908944
Ďalšie názvyHLM, mixed-effects models, random effects models, MLMevaluation research, program assessment, educational evaluation, systematic program evaluation
Príbuzné33
ZhrnutieMultilevel 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.
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ScholarGatePorovnať metódy: Multilevel Modeling · Program Evaluation. Získané 2026-06-15 z https://scholargate.app/sk/compare