방법 비교

	계층적 횡단 연구	다수준 모형
분야	연구설계	연구 통계
계열	Process / pipeline	Process / pipeline
기원 연도	1980s–1990s (formalized with HLM software and methodology)	1992
창시자	Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)	Anthony Bryk and Stephen Raudenbush
유형	Quantitative observational design	Method
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	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
별칭	multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design	HLM, mixed-effects models, random effects models, MLM
관련	2	3
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.	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.