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From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government employees in data analytics. Second we need a careful rethinking of the rules and technical systems that protect data in order to expand access to linked individual-level data across agencies and jurisdictions, while maintaining privacy. Here, we describe a program that has been run for the last three years by the University of Maryland, New York University, and the University of Chicago, with partners such as Ohio State University, Indiana University/Purdue University, Indianapolis, and the University of Missouri. The program—which trains government employees on how to perform applied data analysis with confidential individual-level data generated through administrative processes, and extensive project-focused work—provides both online and onsite training components. Training takes place in a secure environment. The aim is to help agencies tackle important policy problems by using modern computational and data analysis methods and tools. We have found that this program accelerates the technical and analytical development of public sector employees. As such, it demonstrates the potential value of working with individual-level data across agency and jurisdictional lines. We plan to build on this initial success by creating a larger community of academic institutions, government agencies, and foundations that can work together to increase the capacity of governments to make more efficient and effective decisions.

Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targeting the general population. Based on data from the Panel Study Labour Market and Social Security (PASS), an annual probability-based mixed-mode survey on the labor market and poverty in Germany, we find that smartphone ownership and ownership of smartphones with specific OSs are correlated with a number of sociodemographic and substantive variables.

Survey researchers are increasingly seeking opportunities to link interview data with administrative records. However, obtaining consent from all survey respondents (or certain subgroups) remains a barrier to performing record linkage in many studies. We experimentally investigated whether emphasizing different benefits of record linkage to respondents in a telephone survey of employee working conditions improves respondents’ willingness to consent to linkage of employment administrative records relative to a neutral consent request. We found that emphasizing linkage benefits related to “time savings” yielded a small, albeit statistically significant, improvement in the overall linkage consent rate (86.0) relative to the neutral consent request (83.8 percent). The time savings argument was particularly effective among “busy” respondents. A second benefit argument related to “improved study value” did not yield a statistically significant improvement in the linkage consent rate (84.4 percent) relative to the neutral request. This benefit argument was also ineffective among the subgroup of respondents considered to be most likely to have a self-interest in the study outcomes. The article concludes with a brief discussion of the practical implications of these findings and offers suggestions for possible research extensions.

Frauke Kreuter, Joint Program in Survey Methodology

Faculty at the University of Maryland have been working with Facebook to design a worldwide survey aimed at collecting coronavirus data during the global pandemic.

Frauke Kreuter compares the effectiveness of commonly-used face-to-face interview methods

Webinar - will be recorded

Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are used to working, so the assumptions often used to plan and manage a project may no longer hold. For example, social media data are so large that they may not be able to be processed on a single machine; they are in file formats with which many researchers are unfamiliar, and they require a level of data transformation and processing that has rarely been required when using more traditional data sources (e.g., survey data). Unfortunately, this type of information is often not obvious ahead of time as much of this knowledge is gained through word-of-mouth and experience. In this article, we attempt to document several challenges and opportunities encountered when working with Reddit, the self-proclaimed “front page of the Internet” and popular social media site. Specifically, we provide descriptive information about the Reddit site and its users, tips for using organic data from Reddit for social science research, some ideas for conducting a survey on Reddit, and lessons learned in merging survey responses with Reddit posts. While this article is specific to Reddit, researchers may also view it as a list of the type of information one may seek to acquire prior to conducting a project that uses any type of social media data.

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.

Frauke Kreuter and colleagues debate key methodological issues in Public Opinion Quarterly article