পদ্ধতির তুলনা করুন

	শ্রেণিবদ্ধ জরিপ গবেষণা	গুচ্ছ নমুনাচয়ন
ক্ষেত্র	গবেষণা নকশা	জরিপ পদ্ধতি
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1986–1992 (formalization of multilevel methods for nested survey data)	Early-to-mid 20th century; canonical treatment 1953/1977
প্রবর্তক	Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
ধরন	Quantitative survey design with multilevel analysis	Probability sampling design
মৌলিক উৎস	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
অপর নাম	multilevel survey research, nested survey design, multilevel survey design, HLM-based survey research	cluster random sampling, area sampling, one-stage cluster sampling
সম্পর্কিত	6	5
সারসংক্ষেপ	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.	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.