论文标题
多元连续变量的A-最佳拆分问卷设计
A-Optimal Split Questionnaire Designs for Multivariate Continuous Variables
论文作者
论文摘要
拆分问卷设计（SQD）是完整问卷调查的替代方案，可以减轻回答负担并提高调查质量。人们可以设计一份拆分问卷，以减少拆分问卷引起的丢失数据的信息丢失。这项研究开发了一种用于寻找最佳SQD（OSQD）的方法，用于多元连续变量，采用概率设计和最佳标准方法。我们的方法采用先前的调查数据来计算Fisher信息矩阵和A-Oxtimality标准，以找到当前调查研究的OSQD。我们得出了有关相关结构与OSQD与局部OSQD鲁棒性之间关系的理论发现。我们进行仿真研究以比较本地和两个全球OSQD； Mini-Max OSQD和Bayes OSQD）到基线。我们还将我们的方法应用于2016年宠物人群调查（PDS）数据。在模拟研究和实际数据应用中，本地和全局OSQD的表现都优于基准。
A split questionnaire design (SQD), an alternative to full questionnaires, can reduce the response burden and improve survey quality. One can design a split questionnaire to reduce the information loss from missing data induced by the split questionnaire. This study develops a methodology for finding optimal SQD (OSQD) for multivariate continuous variables, applying a probabilistic design and optimality criterion approach. Our method employs previous survey data to compute the Fisher information matrix and A-optimality criterion to find OSQD for the current survey study. We derive theoretical findings on the relationship between the correlation structure and OSQD and the robustness of local OSQD. We conduct simulation studies to compare local and two global OSQDs; mini-max OSQD and Bayes OSQD) to baselines. We also apply our method to the 2016 Pet Demographic Survey (PDS) data. In both simulation studies and the real data application, local and global OSQDs outperform the baselines.
