Responsive Design for Random Digit Dial Surveys Using Auxiliary Survey Process Data and Contextual Data

本研究开发了一个定制的随机数字拨号（RDD）电话调查设计框架，以响应非响应偏差研究的结果。缺乏非应答者的数据是RDD无应答研究中的一个主要挑战，通过使用两种可用的数据来克服这一挑战，而不管应答行为是什么——范式数据和上下文数据。Paradata是由调查过程本身创建的数据，包括对每个抽样号码的所有呼叫的历史记录（例如，拨打的电话数量，呼叫日期和时间）以及每个号码使用的调查设计特征（例如，预先信函，金钱奖励，拒绝转换）。上下文数据来自外部来源（例如，十年一次的人口普查数据），并通过将所有抽样电话号码的地理标识符（例如，人口普查区、邮政编码）链接到相应地理级别的外部数据来创建。这些数据包括相应地点的各种特征，例如人口和社会经济特征，这些特征被假定为近似居住在该地点的个人的特征。本研究将调查反应行为视为一个随机过程，同时受到样本特征、调查特征、情境环境以及这些因素的感知重要性的影响。反应行为是用参数和上下文数据中的变量及其相互作用来建模的。拟合模型用于预测在假设的呼叫时间表和调查设计特征的情况下，响应行为如何变化，从而允许对任何后续调查进行设计裁剪。偏差指标的估计值可以分别为应答者和非应答者计算。无反应偏倚的大小可以通过比较这些估计值来诊断。调查数据是关于人口的可量化信息的重要来源，被政府机构、决策者以及社会、政治和健康科学研究人员广泛使用。尽管回复率不断下降，但考虑到这些调查的受欢迎程度，推进当前的RDD调查实践是重要的。本研究将提供一个新的设计框架，该框架采用整体方法来研究无反应，并结合对无反应的理解。设计裁剪的主要元素是它旨在提高响应率并减少潜在的非响应偏差。这项研究的结果将帮助组织进行RDD调查来改进他们的设计过程。作为支持调查和统计方法研究的联合活动的一部分，该研究得到了方法、测量和统计计划和联邦统计机构联盟的支持。

This study develops a framework for a tailored random digit dial (RDD) telephone survey design that responds to findings from nonresponse bias studies. The lack of data for nonrespondents, a major challenge in RDD nonresponse studies, is overcome by using two types of data available regardless of response behavior - paradata and contextual data. Paradata are data created by the survey process itself and include the history of all calls made to each sampled number (e.g., number of calls placed, calling dates and times) and the survey design features used for each number (e.g., advance letter, monetary incentives, refusal conversion). Contextual data come from external sources (e.g., decennial census data) and are created by linking the geographic identifier (e.g., census tract, ZIP code) of all sampled telephone numbers to the external data at the corresponding geographic level. These data include various characteristics of the corresponding location, such as demographics and socio-economics, which are assumed to approximate the characteristics of individuals residing in that location. The study regards survey response behaviors as a stochastic process influenced simultaneously by the traits of the sample, the survey features, situational circumstances, and the perceived importance of these factors. Response behavior is modeled with variables in the paradata and contextual data and their interactions. The fitted model is used to predict how response behaviors change given hypothetical calling schedules and survey design features, allowing design tailoring for any subsequent surveys. An estimate of a bias indicator can be calculated for respondents and nonrespondents separately. The magnitude of nonresponse bias can be diagnosed by comparing these estimates.Survey data are a vital source for quantifiable information about the population and are widely used by government agencies, policy makers, and social, political, and health science researchers. Advancing the current RDD survey practice is important given the popularity of these surveys in spite of ever-decreasing response rates. This study will provide a new design framework that takes a holistic approach to nonresponse and incorporates the understandings of nonresponse. The major element of the design tailoring is that it aims to increase response rates and decrease potential nonresponse bias. The results of this study will help organizations conducting RDD surveys improve their design process. The research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.