方法对比

	层级调查研究	多层模型
领域	研究设计	研究统计学
方法族	Process / pipeline	Process / pipeline
起源年份	1986–1992 (formalization of multilevel methods for nested survey data)	1992
提出者	Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s	Anthony Bryk and Stephen Raudenbush
类型	Quantitative survey design with multilevel analysis	Method
开创性文献	Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. ISBN: 978-1849202015	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
别名	multilevel survey research, nested survey design, multilevel survey design, HLM-based survey research	HLM, mixed-effects models, random effects models, MLM
相关	6	3
摘要	Hierarchical survey research is a quantitative design that collects survey data from respondents who are naturally nested within higher-level units — such as students within classrooms, employees within organizations, or patients within hospitals — and uses multilevel (hierarchical linear) modeling to analyze variation at each level simultaneously. It is the standard approach whenever survey data have a clustered structure that would violate the independence assumption of ordinary regression.	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.