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Article Reference Troff document (with manpage macros)Coverage Error in Data Collection Combining Mobile Surveys With Passive Measurement Using Apps: Data From a German National Survey
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.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Frauke Kreuter featured in The Baltimore Sun on New Data Collection on COVID-19 with Facebook
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.
Located in News
Article Reference Troff document (with manpage macros)Trust and cooperative behavior: Evidence from the realm of data-sharing
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.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Article Reference Troff document (with manpage macros)Change Through Data: A Data Analytics Training Program for Government Employees
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.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Using propensity scores for causal inference with covariate measurement error
Faculty Associate Frauke Kreuter's project, an R01 funded by the National Institute of Mental Health, seeks to develop and assess new statistical methods
Located in Research / Selected Research
How does interview methodology affect interviewer variance?
Frauke Kreuter compares the effectiveness of commonly-used face-to-face interview methods
Located in Research / Selected Research
Report on Big Data in Survey Research
Frauke Kreuter and colleagues debate key methodological issues in Public Opinion Quarterly article
Located in Research / Selected Research
Errors in Housing Unit Listing and their Effects on Survey Estimates
Frauke Kreuter, Joint Program in Survey Methodology
Located in Resources / / Seed Grant Program / Seed Grants Awarded
Report on Big Data in Survey Research
Public Opinion Quarterly
Located in Resources / / Announcements / Archive
Article Reference Troff document (with manpage macros)Willingness to participate in passive mobile data collection
The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonconsent and nonparticipation. In the current study, we assess the circumstances under which smartphone users are willing to participate in passive mobile data collection. We surveyed 1,947 members of a German nonprobability online panel who own a smartphone using vignettes that described hypothetical studies where data are automatically collected by a research app on a participant’s smartphone. The vignettes varied the levels of several dimensions of the hypothetical study, and respondents were asked to rate their willingness to participate in such a study. Willingness to participate in passive mobile data collection is strongly influenced by the incentive promised for study participation but also by other study characteristics (sponsor, duration of data collection period, option to switch off the app) as well as respondent characteristics (privacy and security concerns, smartphone experience).
Located in MPRC People / Christopher Antoun, Ph.D. / Christopher Antoun Publications