Implementation of weighting system for mixed-mode social surveys

混合模式社会调查加权系统的实施

基本信息

批准号：
16330114
负责人：
MAEDA Tadahiko
金额：
$ 9.86万
依托单位：
The Institute of Statistical Mathematics
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16330114/
关键词：
mode effect in surveys covariate adjustment non-probability sampling random sampling web survey self-administration questionnaire mail survey propensity score

项目摘要

The purpose of the present study was to propose a method for making a weight variable to adjust for the covariate distribution when mixed-mode surveys were administered simultaneously on different subsets of population. We also apply our method to real survey data to discuss its utility. Specifically we pay attention to comparison of survey by mail or face-to-face interview administered to probability samples and web survey administered on non-probability samples. We use the latter to approximate the former. The core of our method is estimating the propensity score, based on the framework of covariate adjustment.As the result of our study, we found that in approximating the results of surveys on probability sample by results of survey done on non-probability samples by different mode, choosing good sets of covariate is very important. We proposed a four-step procedure of variable selection for better covariate adjustment. In addition, we also emphasized that obtaining results of high quality in web survey itself is another key for success in that approximation. We discussed various aspects of quality control in web survey administration. Based on these results, we will further investigate into the task of appropriately integrate the results of mixed-mode surveys into single estimates of population parameter.

本研究的目的是提出一种在对不同人口子集同时进行混合模式调查时制作权重变量以调整协变量分布的方法。我们还将我们的方法应用于实际调查数据来讨论其实用性。具体来说，我们关注对概率样本进行的邮件或面对面访谈调查与对非概率样本进行的网络调查的比较。我们用后者来近似前者。我们方法的核心是基于协变量调整的框架估计倾向得分。我们的研究结果发现，通过不同方法对非概率样本的调查结果来近似概率样本的调查结果。模式，选择好的协变量集非常重要。我们提出了一个四步变量选择程序，以实现更好的协变量调整。此外，我们还强调，获得高质量的网络调查结果本身是该近似成功的另一个关键。我们讨论了网络调查管理中质量控制的各个方面。基于这些结果，我们将进一步研究将混合模式调查的结果适当整合到总体参数的单一估计中的任务。