Порівняння методів

	Оцінка чисельності популяції методом вилову-повторного вилову	Вибірка за спрямуванням респондентів	Стратифікована вибірка
Галузь	Методологія опитувань	Методологія опитувань	Методологія опитувань
Родина	Regression model	Process / pipeline	Process / pipeline
Рік появи	1978	1997	1977
Автор методу	Otis, Burnham, White & Anderson	Douglas Heckathorn	William G. Cochran
Тип	Probabilistic population size estimator	Probabilistic chain-referral sampling design	Probability-based survey sampling design
Основоположне джерело	Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135.	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
Інші назви	Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala	Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Пов'язані	2	3	2
Підсумок	Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records.	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.