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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.

Webinar - will be recorded

Trust is praised by many social scientists as the foundation of functioning social systems owing to its assumed connection to cooperative behavior. The existence of such a link is still subject to debate. In the present study, we first highlight important conceptual issues within this debate. Second, we examine previous evidence, highlighting several issues. Third, we present findings from an original experiment, in which we tried to identify a “real” situation that allowed us to measure both trust and cooperation. People’s expectations and behavior when they decide to share (or not) their data represents such a situation, and we make use of corresponding data. We found that there is no relationship between trust and cooperation. This non-relationship may be rationalized in different ways which, in turn, provides important lessons for the study of the trust—behavior nexus beyond the particular situation we study empirically.

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.

Frauke Kreuter, Joint Program in Survey Methodology

Interviewer-respondent rapport is generally considered to be beneficial for the quality of the data collected in survey interviews; however, the relationship between rapport and data quality has rarely been directly investigated. We conducted a laboratory experiment in which eight professional interviewers interviewed 125 respondents to see how the rapport between interviewers and respondents is associated with the quality of data—primarily disclosure of sensitive information—collected in these interviews. It is possible that increased rapport between interviewers and respondents might motivate respondents to be more conscientious, increasing disclosure; alternatively, increased rapport might inhibit disclosure because presenting oneself unfavorably is more aversive if respondents have a positive relationship with the interviewer. More specifically, we examined three issues: (1) what the relationship is between rapport and the disclosure of information of varying levels of sensitivity, (2) how rapport is associated with item nonresponse, and (3) whether rapport can be similarly established in video-mediated and computer-assisted personal interviews (CAPIs). We found that (1) increased respondents’ sense of rapport increased disclosure for questions that are highly sensitive compared with questions about topics of moderate sensitivity; (2) increased respondents’ sense of rapport is not associated with a higher level of item nonresponse; and (3) there was no significant difference in respondents’ rapport ratings between video-mediated and CAPI, suggesting that rapport is just as well established in video-mediated interviews as it is in CAPI.