Comparer des méthodes

	Échantillonnage stratifié sur le terrain	Échantillonnage en grappes
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1934 (Neyman's stratified sampling theory); field applications throughout 20th century	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Probability sampling design	Probability sampling design
Source fondatrice	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alias	field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	6	5
Résumé	Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set.	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.