So sánh phương pháp

	Chọn mẫu cụm dựa trên thực địa	Chọn mẫu phân tầng dựa trên thực địa
Lĩnh vực	Phương pháp luận khảo sát	Phương pháp luận khảo sát
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1950s (theory); 1970s–1980s (field survey practice)	1934 (Neyman's stratified sampling theory); field applications throughout 20th century
Người khởi xướng	William G. Cochran (theoretical foundations); WHO EPI programme (field application)	Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice
Loại	Probability sampling design	Probability sampling design
Công trình gốc	World Health Organization. (1991). Training for mid-level managers: The EPI coverage survey. WHO/EPI/MLM/91.10. World Health Organization.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Tên gọi khác	field cluster sampling, in-field cluster sampling, area cluster sampling (field), field survey cluster design	field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification
Liên quan	6	6
Tóm tắt	Field-based cluster sampling is a probability sampling method in which naturally occurring geographic or administrative groups (clusters) are first randomly selected, and then data are collected in person from units within those clusters. It is the standard design for large-scale field surveys in public health, agriculture, education, and humanitarian response, where compiling a full population list is impractical but clusters such as villages, schools, or census tracts can be identified and physically accessed.	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.