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

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.