Methoden vergleichen

	Hierarchische deskriptive Forschung	Cluster-Sampling
Fachgebiet	Forschungsdesign	Umfragemethodik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–1990s (multilevel descriptive formalization)	Early-to-mid 20th century; canonical treatment 1953/1977
Urheber	Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Typ	Quantitative observational/descriptive design	Probability sampling design
Wegweisende Quelle	Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728455	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Aliasnamen	multilevel descriptive design, nested descriptive study, hierarchical survey design, stratified descriptive research	cluster random sampling, area sampling, one-stage cluster sampling
Verwandt	4	5
Zusammenfassung	Hierarchical descriptive research is an observational design that documents the current state of a phenomenon across two or more nested levels — for example, students within classrooms within schools, or employees within teams within organizations. Rather than testing hypotheses or explaining causation, it describes distributions, frequencies, and relationships at each level, making explicit the structured, layered nature of the population being studied.	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.