Salīdzināt metodes

	Klasteru izlase	Daudzlīmeņu modelēšana
Nozare	Aptauju metodoloģija	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	Early-to-mid 20th century; canonical treatment 1953/1977	1992
Autors	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Anthony Bryk and Stephen Raudenbush
Tips	Probability sampling design	Method
Pirmavots	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	cluster random sampling, area sampling, one-stage cluster sampling	HLM, mixed-effects models, random effects models, MLM
Saistītās	5	3
Kopsavilkums	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.	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.