Сравнение методов

	Кластерный рандомизированный лабораторный эксперимент	Многоуровневое моделирование
Область	Планирование эксперимента	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s (formalized; cluster randomization principles developed in 1970s-1980s)	1992
Автор метода	David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design	Anthony Bryk and Stephen Raudenbush
Тип	Controlled laboratory experiment with cluster-level randomization	Method
Основополагающий источник	Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120363	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Другие названия	cluster-randomized lab experiment, group-randomized laboratory study, cluster RCT laboratory variant, clustered lab trial	HLM, mixed-effects models, random effects models, MLM
Связанные	6	3
Сводка	A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting.	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.