Сравнение методов

	Иерархическое подтверждающее исследование	Многоуровневое моделирование
Область	Дизайн исследования	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1980s–2000s	1992
Автор метода	Raudenbush & Bryk; Hox; Goldstein	Anthony Bryk and Stephen Raudenbush
Тип	Quantitative confirmatory research design	Method
Основополагающий источник	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Другие названия	multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR	HLM, mixed-effects models, random effects models, MLM
Связанные	5	3
Сводка	Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent.	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.