方法对比

	自适应配额抽样	自适应分层抽样	配额抽样	分层抽样
领域	调查方法论	调查方法论	调查方法论	调查方法论
方法族	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2000s (adaptive extension of quota principles)	1990s (formal development from Thompson 1990 onward)	1930s	1977
提出者	Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006)	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others	Developed in market research and opinion polling, notably applied by George Gallup in the 1930s	William G. Cochran
类型	Non-probability sampling with adaptive control	Probability-based adaptive sampling design	Non-probability sampling design	Probability-based survey sampling design
开创性文献	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.	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	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
别名	responsive quota sampling, dynamic quota sampling, iterative quota sampling	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling	quota-controlled sampling, quota selection, non-probability quota sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
相关	3	6	5	2
摘要	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.	Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present.	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.