เปรียบเทียบวิธี

	การสุ่มตัวอย่างแบบกลุ่มปรับเปลี่ยน (Adaptive Cluster Sampling)	การชักชวนผู้ตอบแบบสอบถามแบบลูกโซ่	การสุ่มแบบแบ่งชั้น
สาขาวิชา	ระเบียบวิธีสำรวจ	ระเบียบวิธีสำรวจ	ระเบียบวิธีสำรวจ
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	1990	1997	1977
ผู้ริเริ่ม	Steven Thompson	Douglas Heckathorn	William G. Cochran
ประเภท	Probability-based adaptive design	Probabilistic chain-referral sampling design	Probability-based survey sampling design
แหล่งต้นตำรับ	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
ชื่อเรียกอื่น	Adaptive Cluster Sampling, Sequential Adaptive Sampling, Network Sampling, Adaptif Küme Örneklemesi	Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
ที่เกี่ยวข้อง	3	3	2
สรุป	Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.	Respondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.