পদ্ধতির তুলনা করুন

	অভিযোজিত কোটা স্যাম্পলিং	অভিযোজিত স্তরবিন্যস্ত নমুনাচয়ন (Adaptive Stratified Sampling)	স্তরবিন্যস্ত নমুনায়ন (Stratified Sampling)
ক্ষেত্র	জরিপ পদ্ধতি	জরিপ পদ্ধতি	জরিপ পদ্ধতি
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000s (adaptive extension of quota principles)	1990s (formal development from Thompson 1990 onward)	1977
প্রবর্তক	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	William G. Cochran
ধরন	Non-probability sampling with adaptive control	Probability-based adaptive sampling design	Probability-based survey sampling design
মৌলিক উৎস	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.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
অপর নাম	responsive quota sampling, dynamic quota sampling, iterative quota sampling	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
সম্পর্কিত	3	6	2
সারসংক্ষেপ	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.	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.