방법 비교

	계층적 횡단 연구	군집 표본 추출
분야	연구설계	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1980s–1990s (formalized with HLM software and methodology)	Early-to-mid 20th century; canonical treatment 1953/1977
창시자	Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
유형	Quantitative observational design	Probability sampling design
원전	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
별칭	multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design	cluster random sampling, area sampling, one-stage cluster sampling
관련	2	5
요약	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.