Skip to content. | Skip to navigation

Personal tools

Navigation

You are here: Home

Search results

13 items matching your search terms.
Filter the results.
Item type









































New items since



Sort by relevance · date (newest first) · alphabetically
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
Article Reference Troff document (with manpage macros)Tree-based Machine Learning Methods for Survey Research
Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications
Article ReferenceThe effect of framing and placement on linkage consent
Numerous surveys link interview data to administrative records, conditional on respondent consent, in order to explore new and innovative research questions. Optimizing the linkage consent rate is a critical step toward realizing the scientific advantages of record linkage and minimizing the risk of linkage consent bias. Linkage consent rates have been shown to be particularly sensitive to certain design features, such as where the consent question is placed in the questionnaire and how the question is framed. However, the interaction of these design features and their relative contributions to the linkage consent rate have never been jointly studied, raising the practical question of which design feature (or combination of features) should be prioritized from a consent rate perspective. We address this knowledge gap by reporting the results of a placement and framing experiment embedded within separate telephone and Web surveys. We find a significant interaction between placement and framing of the linkage consent question on the consent rate. The effect of placement was larger than the effect of framing in both surveys, and the effect of framing was only evident in the Web survey when the consent question was placed at the end of the questionnaire. Both design features had negligible impact on linkage consent bias for a series of administrative variables available for consenters and non-consenters. We conclude this research note with guidance on the optimal administration of the linkage consent question.
Located in MPRC People / Frauke Kreuter, Ph.D. / Frauke Kreuter Publications