Estimating Propensity Adjustments for Volunteer Web Surveys
Estimating Propensity Adjustments for Volunteer Web Surveys
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DOI：
10.1177/0049124110392533
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发表时间：
2011-02-01
影响因子：
6.3
通讯作者：
Dever, Jill A.
中科院分区：
文献类型：
--
作者：
Valliant, Richard;Dever, Jill A.
利用自愿参加网上调查的人员组成的小组对整个人口，包括无法上网的人进行估计。调整志愿者样本以使其代表更大的群体的一种方法涉及从更大的群体中随机选择参考样本。志愿服务行为被视为一个准随机过程，每个人都有一定的志愿服务概率。计算志愿者权重的一个选择是将参考样本和网络志愿者联合收割机结合，并通过倾向建模来估计成为网络志愿者的概率。有几种方法可以使用估计的倾向来估计人口数量。缺乏仔细的分析来证明这些方法的合理性。本文的目标是：（a）确定估计的假设和技术，这将导致正确的推断下的准随机方法，（B），以探讨是否在实践中使用的方法是有偏的，（c）说明一些估计的性能，使用估计的倾向。我们的两个主要发现是：（a）即使志愿服务的概率被正确建模，基于不使用与参考样本相关的权重的倾向模型估计的均值估计值也会有偏差;（B）如果志愿服务的概率与志愿者调查中收集的分析变量相关，则倾向模型不会纠正偏差。
Panels of persons who volunteer to participate in Web surveys are used to make estimates for entire populations, including persons who have no access to the Internet. One method of adjusting a volunteer sample to attempt to make it representative of a larger population involves randomly selecting a reference sample from the larger population. The act of volunteering is treated as a quasi-random process where each person has some probability of volunteering. One option for computing weights for the volunteers is to combine the reference sample and Web volunteers and estimate probabilities of being a Web volunteer via propensity modeling. There are several options for using the estimated propensities to estimate population quantities. Careful analysis to justify these methods is lacking. The goals of this article are (a) to identify the assumptions and techniques of estimation that will lead to correct inference under the quasi-random approach, (b) to explore whether methods used in practice are biased, and (c) to illustrate the performance of some estimators that use estimated propensities. Two of our main findings are (a) that estimators of means based on estimates of propensity models that do not use the weights associated with the reference sample are biased even when the probability of volunteering is correctly modeled and (b) if the probability of volunteering is associated with analysis variables collected in the volunteer survey, propensity modeling does not correct bias.