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）的电话调查设计，响应从无响应偏差研究的结果的框架。 缺乏数据的nonresponses，在RDD nonresponse研究的一个主要挑战，克服了使用两种类型的数据，无论响应行为-paradise和上下文数据。 Paramount是由调查过程本身创建的数据，并且包括对每个采样号码进行的所有呼叫的历史（例如，发出的呼叫的数量、呼叫日期和时间）以及用于每个号码的调查设计特征（例如，提前函、货币奖励、拒绝转换）。 上下文数据来自外部源（例如，十年一次的普查数据）并且通过链接地理标识符（例如，人口普查区，邮政编码）的所有抽样电话号码的外部数据在相应的地理水平。 这些数据包括相应位置的各种特征，例如人口统计学和社会经济学，这些特征被假定为近似居住在该位置的个人的特征。 本研究将调查响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。 响应行为是用paramount和上下文数据中的变量及其交互来建模的。 拟合模型用于预测响应行为如何改变给定的假设调用时间表和调查设计功能，允许为任何后续调查定制设计。 可以分别计算受访者和非受访者的偏倚指标估计值。 通过比较这些估计值可以诊断出无应答偏倚的程度。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、政策制定者以及社会、政治和健康科学研究人员广泛使用。 尽管答复率不断下降，但鉴于这些调查的普及，推进目前的RDD调查做法很重要。 这项研究将提供一个新的设计框架，采取整体的方法来无反应，并纳入无反应的理解。 设计剪裁的主要元素是它旨在提高应答率和降低潜在的无应答偏倚。 这项研究的结果将有助于组织进行RDD调查，改善他们的设计过程。 这项研究得到了方法、测量和统计方案以及联邦统计机构联合会的支持，作为支持调查和统计方法研究的联合活动的一部分。

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.