Comparer des méthodes

	Échantillonnage stratifié sur le terrain	Échantillonnage stratifié proportionnel
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	1953–1965 (formalized in survey sampling literature)
Auteur d'origine	Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice	William G. Cochran; Leslie Kish
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.). John Wiley & Sons. ISBN: 978-0471162407
Alias	field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification	proportionate stratified sampling, proportional allocation stratified sampling, PSRS, proportionate stratified random sampling
Apparentées	6	6
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.	Proportional stratified sampling divides the target population into non-overlapping strata (subgroups defined by a key characteristic such as age band, region, or gender) and then draws a simple random sample from each stratum so that each stratum's share of the total sample matches its share of the total population. Because each subgroup is represented in exact proportion to its population weight, the resulting sample mirrors the population structure closely without requiring post-hoc weighting adjustments.