השוואת שיטות

	דגימת מכסה אדפטיבית	דגימת מכסות	דגימה שכבתית
תחום	מתודולוגיית סקרים	מתודולוגיית סקרים	מתודולוגיית סקרים
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2000s (adaptive extension of quota principles)	1930s	1977
הוגה השיטה	Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006)	Developed in market research and opinion polling, notably applied by George Gallup in the 1930s	William G. Cochran
סוג	Non-probability sampling with adaptive control	Non-probability sampling design	Probability-based survey sampling design
מקור מכונן	Groves, R. M., & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society: Series A, 169(3), 439–457.	Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann. ISBN: 978-0435827496	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
כינויים	responsive quota sampling, dynamic quota sampling, iterative quota sampling	quota-controlled sampling, quota selection, non-probability quota sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
קשורות	3	5	2
תקציר	Adaptive quota sampling is a non-probability sampling approach that starts with predefined demographic or characteristic-based quotas and then adjusts those quotas during data collection in response to emerging response patterns, nonresponse trends, or representativeness concerns. By treating the sampling process as iterative rather than fixed, it allows researchers to correct imbalances in real time and improve the final sample composition without restarting data collection from scratch.	Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference.	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.