摘要
抽样调查在大数据时代仍是不可或缺的研究工具.然而,传统调查方式当前面临执行成本增加与数据质量降低的双重挑战.作为降低受访者负担的有效途径,问卷分割设计逐渐受到研究者重视.文章研究针对问卷分割设计中的子问卷分配过程展开讨论:在假设受访者招募服从泊松过程前提下,以降低协变量的子样本间差异为目标设计成组序贯随机过程.理论和数值分析显示该过程相较现有随机化方法具有优良的表现,可以更好地平衡子样本间协变量差异并提高估计量的估计精度.
Sampling survey is still an essential tool in the era of big data.However,traditional sampling survey faces the dual challenges of increasing execution cost and decreasing data quality.Split questionnaire design can has been paid more attention by researchers as an effective way to reduce the cost and improve the data quality.In this paper,we discuss the sub-questionnaire assignment process in the split questionnaire design.Based on the assumption that participants arrive in accordance with the Poisson process,the sequential randomization method considering covariates balance is designed with the goal of improving the similarity among sub-samples and the population.Both theoretical and numerical results show that the proposed method has superior performance compared with the existing methods on sub-sample balancing and estimation accuracy.
作者
杨昊宇
秦祎辰
李扬
YANG Haoyu;QIN Yichen;LI Yang(School of Statistics,Renmin University of China,Beijing 100872;Department of Operations,Business Analytics,and Information Systems,University of Cincinnati,Cincinnati 45221;Center for Applied Statistics,School of Statistics,Renmin University of China,Beijing 100872)
出处
《系统科学与数学》
CSCD
北大核心
2022年第1期17-34,共18页
Journal of Systems Science and Mathematical Sciences
基金
国家自然科学基金面上项目(71771211)
全国统计科学研究项目重大项目(2019LD07)资助课题。
关键词
抽样调查
问卷分割
子问卷分配
协变量平衡
成组序贯随机化
Sampling survey
split questionnaire design
sub-questionnaire allocation
covariate balance
group sequential randomization
