เปรียบเทียบวิธี

	การวิจัยสำรวจเชิงลำดับชั้น	การวิจัยสำรวจเชิงเปรียบเทียบ
สาขาวิชา	การออกแบบการวิจัย	การออกแบบการวิจัย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1986–1992 (formalization of multilevel methods for nested survey data)	Mid-20th century onward
ผู้ริเริ่ม	Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s	Rooted in survey methodology traditions (Gallup, Likert, Lazarsfeld mid-20th century); comparative extension codified in social science research methods literature
ประเภท	Quantitative survey design with multilevel analysis	Quantitative non-experimental research design
แหล่งต้นตำรับ	Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. ISBN: 978-1849202015	Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000
ชื่อเรียกอื่น	multilevel survey research, nested survey design, multilevel survey design, HLM-based survey research	comparative survey design, cross-group survey, multi-group survey research, comparative questionnaire study
ที่เกี่ยวข้อง	6	4
สรุป	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.	Comparative survey research is a quantitative non-experimental design that systematically collects structured survey data from two or more clearly defined groups, populations, or contexts in order to identify, describe, and analyze similarities and differences among them. It extends basic survey research by making comparison the explicit organizing logic: rather than characterizing a single population, the goal is to detect how attitudes, behaviors, or outcomes vary across groups defined by nationality, culture, profession, demographic category, or time period.