方法对比

	层级横断面研究	簇抽样	多层模型
领域	研究设计	调查方法论	研究统计学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1980s–1990s (formalized with HLM software and methodology)	Early-to-mid 20th century; canonical treatment 1953/1977	1992
提出者	Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Anthony Bryk and Stephen Raudenbush
类型	Quantitative observational design	Probability sampling design	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	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
别名	multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design	cluster random sampling, area sampling, one-stage cluster sampling	HLM, mixed-effects models, random effects models, MLM
相关	2	5	3
摘要	Hierarchical cross-sectional research is a quantitative observational design that collects data from individuals nested within higher-level units — such as students within schools, patients within hospitals, or employees within organizations — at a single point in time. By accounting for the non-independence of clustered observations through multilevel modeling, it enables researchers to simultaneously examine individual-level and group-level predictors of an outcome without violating the independence assumption of ordinary regression.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.	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.