So sánh phương pháp

	Trọng số khảo sát và hiệu chỉnh	Multiple Imputation	Lấy mẫu phân tầng
Lĩnh vực	Phương pháp luận khảo sát	Thống kê	Phương pháp luận khảo sát
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	2010	1987	1977
Người khởi xướng	Sharon Lohr	Donald B. Rubin	William G. Cochran
Loại	Estimation adjustment procedure	Missing-data handling procedure	Probability-based survey sampling design
Công trình gốc	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Tên gọi khác	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Liên quan	3	1	2
Tóm tắt	Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.	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.