So sánh phương pháp

	Khảo sát Quan hệ Phân cấp	Nghiên cứu cắt ngang phân cấp
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1980s–2002 (modern HLM-based survey tradition)	1980s–1990s (formalized with HLM software and methodology)
Người khởi xướng	Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis)	Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)
Loại	Quantitative survey design with multilevel relational analysis	Quantitative observational design
Công trình gốc	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. ISBN: 978-1849202015
Tên gọi khác	nested relational survey, multilevel relational survey, HLM-based relational survey, hierarchical correlational survey	multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design
Liên quan	4	2
Tóm tắt	A hierarchical relational survey combines the correlational goals of relational survey research with a multilevel data structure in which respondents are nested within higher-level units such as classrooms, schools, hospitals, or organizations. The design acknowledges that observations within the same group are not independent, and uses hierarchical linear modeling (HLM) or equivalent multilevel techniques to examine relationships among variables both within and between levels simultaneously.	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.