قارن الطرق

	تقدير حجم السكان بطريقة الالتقاط وإعادة الالتقاط	أخذ العينات بالاستعانة بالشبكات (Respondent-Driven Sampling)	العينات الطبقية
المجال	منهجية المسح	منهجية المسح	منهجية المسح
العائلة	Regression model	Process / pipeline	Process / pipeline
سنة النشأة	1978	1997	1977
صاحب الطريقة	Otis, Burnham, White & Anderson	Douglas Heckathorn	William G. Cochran
النوع	Probabilistic population size estimator	Probabilistic chain-referral sampling design	Probability-based survey sampling design
المصدر التأسيسي	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
الأسماء البديلة	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
ذات صلة	2	3	2
الملخص	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.