Chair for Statistics and Data Science in Social Sciences and the Humanities (SODA)
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Current Projects

Overview

The Covid-19 Pandemic and Data Sharing for the Public Good

CAIUS: Consequences of AI for Urban Societies

BERD – Business and Economics Data Center (BERD@BW)

FairADM: Fairness and discrimination in automated decision-making processes

Trust when Sharing Data Online

New Methods for Job and Occupation Classification

IAB-SMART: Collecting Data for Labor Market Research Through a Smartphone App

Concerns and Willingness to Use Smartphones for Data Collection

Reddit data as a new tool and source for social research

Supplementing and substituting survey data with big data

Modernizing Migration Measures: Combining Survey and Tracking Data Collection Among Asylum-Seeking Refugees

International Program in Survey Practice and Data Science

 

The Covid-19 Pandemic and Data Sharing for the Public Good

Does the Covid-19 pandemic lead to a temporary or persistent shift in acceptance of data sharing practices for public benefit purposes? Under which conditions to individuals deem data sharing for the public good as acceptable or even desirable? How can we accomodate data collection and sharing practices and policy to the meet the public's preferences? In the project "The Covid-19 Pandemic and Data Sharing for the Public Good: Attitudinal, Ethical, and Legal Approaches to Privacy During the Pandemic and Beyond" wconduct international surveys at multiple time points to measure acceptance of different data sharing scenarios. We investigate how specific situational parameters of the scenarios affect acceptance and how acceptance changes over time, while the purpose of data collection is of particular interest. The project adds to our understanding of the context dependence of privacy attitudes and, by involving ethical and legal science, informs data collectors and policy-makers on how to design practices and policies such that data for the public good is made possible in an ethical manner. This project is funded by Volkswagen Foundation.

  • Project team: Prof. Dr. Frauke Kreuter, Prof. Dr. Thomas Fetzer, Prof. Helen Nissenbaum, PhD, Frederic Gerdon

 

CAIUS: Consequences of AI for Urban Societies

AI systems help to efficiently allocate scarce public resources and are at the core of many smart city activities. Yet, the same systems may also result in unintended societal consequences, particularly by reinforcing social inequalities. CAIUS will identify and analyze such consequences. Using agent-based models (ABM), the effects of AI-based decisions on societal macro variables of social inequality such as income disparity will be analyzed. The data input for these ABMs consists of both Open Government Data and own surveys. The goal is to train AI systems to account for their social consequences within specific fairness constraints; this synthesis of ABM and fair reinforcement learning lays the groundwork for what we call „impact-aware AI“ in urban contexts. With CAIUS, two smart city applications planned by partners in the Rhine-Neckar Metropolitan Region will be accompanied: dynamic pricing of parking space and traffic law enforcement via Internet-of-Things sensors. The results will contribute to the research of human-AI interaction and will be condensed into general guidelines for decision-makers regarding the ethical implementation of AI-based decision-making systems in urban contexts.

  • Project team: Ruben Bach, Prof. Dr. Frauke Kreuter, Dr. Christoph Kern, Frederic Gerdon
  • Publications: Gerdon, F., Theil, C. K., Kern, C., Bach, R. L., Kreuter, F., Stuckenschmidt, H. und Eckert, K. (2020). Exploring impacts of artificial intelligence on urban societies with social simulations. 40. Kongress der Deutschen Gesellschaft für Soziologie, Online.

BERD – Business and Economics Data Center (BERD@BW)

The aim of BERD@BW is to establish a competence center for data availability, data exchange, and data analysis. The project also includes the development of training and further education. These courses will use concrete case studies to teach skills that researchers need for data-based work. Thanks to its extensive experience with the International

  • Project team: Prof. Dr. Frauke Kreuter, Markus Herklotz

FairADM: Fairness and discrimination in automated decision-making processes

The project „Fairness in Automated Decision-Making (Fair ADM)“ by Prof. Dr. Frauke Kreuter, Dr. Ruben Bach, and Dr. Christoph Kern deals with discrimination and fairness of algorithm-based decision-making processes (Automated Decision-Making, ADM) in the German public sector. „While ADM systems optimize bureaucratic procedures through automation, their use also raises new social and ethical questions,“ says Prof. Dr. Frauke Kreuter. It is feared that ADM could increase existing social discrimination. For example, ADM systems are already being used in the U.S. to assess the risk of recidivism of defendants in the context of legal proceedings. A particularly sensitive field of application of ADM in the European context is the assessment of job seekers' chances on the labor market, e.g. for the allocation of training resources, which has recently been proposed by the Austrian Public Employment Service (AMS). There is a risk that sensitive characteristics such as gender, age, or marital status are brought into the algorithmic decision-making process and thus influence the distribution of resources. In order to shed more light on this and to empirically investigate methods to correct unfair algorithms, the project develops and evaluates an ADM based on administrative labor market data. This research is supported with 171.000 Euro.

  • Project team: Prof. Dr. Frauke Kreuter, Christoph Kern, and Ruben Bach

Trust when Sharing Data Online

Decisions about confidentiality protection measures to be applied to data dissemination must be informed by evidence about the utility associated with the quality of the data and the willingness to trade utility against the estimated risk. Doing so requires measurement of data utility, risk, and the willingness of individuals to trade risk for utility. From the theoretical literature on measuring privacy (Nissenbaum 2011) and trust (Bauer and Freitag 2018), perceptions of trust and privacy are context-dependent. There are three dimensions that are particular important: (1) to whom the data is provided, (2) what is done with the data (i.e., whether there are benefits for the one receiving the data vs. benefits for the one providing the data), and (3) what kind of data is shared (i.e., the sensitivity of the data). Some data are inherently sensitive because they touch taboo topics (e.g., information on income, sexual behavior, etc.), other data is only sensitive if it reveals specific information about illegal (e.g., illicit drug use) or counter-normative behaviors and attitudes (Tourangeau and Yan 2007). In this project, we measure utility, risk, and tradeoffs in the context of privacy and data sharing in several cross-sectional surveys. The data landscape has dramatically changed in May of 2018 when GDPR came into effect, and with it the control people have about their data, and the risks companies face when violating GDPR. Thus, we also collect longitudinal data on the awareness about the GDPR regulations in Germany, and in an experimental setting, we measure the influence of GDPR information on trust in various data collecting organizations.

  • Project team: Prof. Dr. Frauke Kreuter, Prof. Dr. Florian Keusch, and Paul C. Bauer
  • Publications: Bauer, P. C., Keusch, F. & Kreuter, F. (2019). Trust and cooperative behavior: Evidence from the realm of data-sharing. PLOS ONE, 14(8), e0220115. https://doi.org/10.1371/journal.pone.0220115

New Methods for Job and Occupation Classification

Currently, most surveys ask for occupation with open-ended questions. The verbatim responses are coded afterward into a classification with hundreds of categories and thousands of jobs, which is an error-prone, time-consuming, and costly task. When textual answers have a low level of detail, exact coding may be impossible. The project investigates how to improve this process by asking response-dependent questions during the interview. Candidate job categories are predicted with a machine learning algorithm and the most relevant categories are provided to the interviewer. Using this job list, the interviewer can ask for more detailed information about the job. The proposed method is tested in a telephone survey conducted by the Institute for Employment Research (IAB). Administrative data are used to assess the relative quality resulting from traditional coding and interview coding. This project is done in cooperation with Arne Bethmann (IAB, University of Mannheim), Manfred Antoni (IAB), Markus Zielonka (LIfBi), Daniel Bela (LIfBi), and Knut Wenzig (DIW).

IAB-SMART: Collecting Data for Labor Market Research Through a Smartphone App

Smartphones are multifunctional tools, which can be used for personal communication, planning, entertainment, information search, and many other things in our daily lives. Many people cannot imagine a life without their smartphones, and they carry them around with them all the time. The omnipresence of smartphones makes these devices interesting for researchers who want to collect data to measure human behavior through sensors built-in on a smartphone. Together with the Institute for Employment Research (IAB) we developed the IAB-SMART app to evaluate the opportunities and challenges when using smartphones for data collection in social research, more specifically on labor market research. The IAB-SMART app passively collects mobile data, such as geolocation of users, activities, social interactions, and online behavior, and launches in-app surveys. In addition, we are able to combine these data (given the user’s consents) with survey data from a longstanding panel survey (PASS) and administrative data from the Institute for Employment Research (IAB) containing the employment history of users. The passive measures allow researchers to take a wider perspective on labor market-related behavior such as home office productivity and job search strategies. Furthermore, the combination of sensor, survey, and administrative data will help us to understand how (un)employment affects daily life. In addition to these substantial questions, this project helps us answer methodological research questions on the quality of the data collected through this method.

Project Team: Prof. Dr. Frauke Kreuter, Prof. Dr. Florian Keusch, Georg-Christoph Haas, Prof. Dr. Mark Trappmann, Sebastian Bähr 

Publications:  Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F. & Trappmann, M. (2020). Missing data and other measurement quality issues in mobile geolocation sensor data. Social Science Computer Review : SSCORE, 1–24. https://doi.org/10.1177/0894439320944118

Haas, G.-C., Kreuter, F., Keusch, F., Trappmann, M. & Bähr, S. (2020). Effects of incentives in smartphone data collection. In C. A. Hill (eds.), Big data meets survey science : a collection of innovative methods (S. 387–414). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch13

Haas, G.-C., Trappmann, M., Keusch, F., Bähr, S. & Kreuter, F. (2020). Using geofences to collect survey data: Lessons learned from the IAB-SMART study. Survey Methods : Insights from the Field, 2020(10/12/20), 1–12. https://doi.org/10.13094/SMIF-2020-00023

Keusch, F., Bähr, S., Haas, G.-C., Kreuter, F. & Trappmann, M. (2020). Coverage error in data collection Ccombining mobile surveys with passive measurement using apps: Data from a German national survey. Sociological Methods & Research : SMR. https://doi.org/10.1177/0049124120914924

Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S. & Trappmann, M. (2019). Collecting survey and smartphone sensor data with an App: Opportunities and challenges around privacy and informed consent. Social Science Computer Review : SSCORE, 38(5), 533–549. https://doi.org/10.1177/0894439318816389

Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F. & Trappmann, M. (2018). IAB-SMART-Studie: Mit dem Smartphone den Arbeits­markt erforschen. IAB-Forum : Das neue Onlinemagazin des Instituts für Arbeits­markt- und Berufsforschung, 2018, 09.01.2018.

Concerns and Willingness to Use Smartphones for Data Collection

Smartphone use is on the rise worldwide, and researchers are exploring novel ways to leverage the capabilities of smartphones for data collection. Mobile surveys, i.e., surveys that are filled out on a smartphone web browser or through an app, are already extensively studied. Research on the use of other features of smartphones that allow researchers to automatically measure an even broader set of characteristics and behaviors of users that go far beyond the collection of mere self-reports is still in its infancy. For example, smartphone users can now be asked to take pictures of receipts to better measure expenditure, to agree to tracking of movements to create exact measures of mobility and transportation or to automatically log app use, Internet searches, and phone calling and text messaging behavior to measure social interaction. These forms of data collection provide richer data (because it can be collected in much higher frequencies compared to self-reports) and have the potential to decrease respondent burden (because fewer survey questions need to be asked) and measurement error (because of reduction in recall errors and social desirability). However, agreeing to engage in these forms of data collection from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing specific data with researchers due to security, privacy, and confidentiality concerns. Moreover, users might have differential concerns with different types of data collection on smartphones, and thus be more willing to engage in some of these data collection tasks than in others. In addition, participants might differ in their skills of smartphone use and thus feel more or less comfortable using smartphones for research, leading to bias due to differential nonparticipation of specific subgroups. In a series of studies, we measure concerns and willingness when it comes to participation in smartphone data collection.

Project Team: Prof. Dr. Frauke Kreuter, Prof. Dr. Florian Keusch, Bella Struminskaya, Mick Couper and Christopher Antoun

Publications: Keusch, F., Struminskaya, B., Kreuter, F. & Weichbold, M. (2020). Combining active and passive mobile data collection : A survey of concerns. In C. A. Hill (eds.), Big data meets survey science : a collection of innovative methods (S. 657–682). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch22

Struminskaya, B. & Keusch, F. (2020). Editorial: From web surveys to mobile web to apps, sensors, and digital traces. Survey Methods : Insights from the Field, 2020(10/12/20), 1–7. https://doi.org/10.13094/SMIF-2020-00015

Struminskaya, B., Lugtig, P., Keusch, F. & Höhne, J. K. (2020). Augmenting surveys with data from sensors and apps: Opportunities and challenges. Social Science Computer Review : SSCORE. https://doi.org/10.1177/0894439320979951

Keusch, F., Struminskaya, B., Antoun, C., Couper, M. P. & Kreuter, F. (2019). Willingness to participate in passive mobile data collection. Public Opinion Quarterly : POQ, 83(S1), 210–235. https://doi.org/10.1093/poq/nfz007

Modernizing Migration Measures: Combining Survey and Tracking Data Collection Among Asylum-Seeking Refugees

Collecting information about refugees is necessary to guide policy makers in creating sustainable integration concepts and to increase the scientific understanding of migration and integration processes in general. However, interviewing refugees in immigration reception centers and following them in a longitudinal study can be difficult. In this project, we assess the feasibility of data collection via smartphones among refugees in Germany. While using smartphones to collect mobile web survey data has become increasingly popular over the last couple of years, combining these data with automatic tracking of online behavior and geolocation of the smartphone is a novel approach that requires thorough empirical testing. The project provides both methodological insight into how to utilize smartphone data collection (combining survey and tracking data) and much-needed scientifically based knowledge on the needs, aspirations, and life circumstances of refugees in Germany.

  • Project team: Prof. Dr. Florian Keusch, Mariel Leonard, Christoph IMF – University of Mannheim and Susan IZA
  • Publications: Keusch, F., Leonard, M. M., Sajons, C. & Steiner, S. (2019). Using smartphone technology for research on refugees: Evidence from Germany. Sociological Methods & Research : SMR, 1–32. https://doi.org/10.1177/0049124119852377

Reddit data as a new tool and source for social research

The use of non-traditional data (i.e., data collected from non-probability sample surveys, passive data, or Big Data) to supplement or replace survey data is growing. However, these data are not without weaknesses; they suffer from their own sources of error, access challenges, and confidentiality concerns. This project uses survey data collected on and posts scraped from Reddit.com to answer three research questions: 1) Can social media data be used to accurately assess social attitudes? 2) What are the sources of error in social media data? 3) What variability in the conclusions drawn from these data is introduced by the researcher’s choice in analytic methods? In addition to the research questions, this project also offers some descriptions of the data and access to it so future Reddit data users can further refine their budgets, timelines, and expectations.

Project Team: Ruben Bach, Ashley Amaya, Prof. Dr. Frauke Kreuter, Prof. Dr. Florian Keusch and Vlad Achimescu

Publications:  Achimescu, V. und Chachev, P. D. (2021). Raising the flag: Monitoring user-perceived dis­information on reddit. Information, 12, 4. https://www.mdpi.com/2078-2489/12/1/4

Amaya, A., Bach, R. L., Keusch, F. und Kreuter, F. (2019). New data sources in social science research: Things to know before working with Reddit data. Social Science Computer Review : SSCORE, 1-10. https://doi.org/10.1177/0894439319893305

Amaya, A., Bach, R. L., Kreuter, F. und Keusch, F. (2020). Measuring the strength of attitudes in social media data. In Big data meets survey science : a collection of innovative methods (S. 163-192). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch5

Supplementing and substituting survey data with big data

For many years, surveys were the standard tool to measure attitudes and behavior for social science research. In recent years, however, researchers have shifted their focus to new sources of data, especially in the online world. For instance, researchers have analyzed the potentials of replacing or supplementing survey data with data from Twitter, smart devices (e.g., smartphones or fitness tracker) and data from other places where people leave digital traces. In this project, we explore the feasibility of using behavioral records of individuals’ online activities to study political attitudes and behavior. Specifically, we explore the potentials of online behavioral data to substitute traditional survey data by inferring attitudes and behavior from the online data. In addition, we analyze how complete such data are as users may switch off data collection during certain activities they do not want recorded. Moreover, we study how (social) media use shapes attitudes and behavior in the offline world. This project is done in collaboration with Ashley Amaya (RTI International).

Project Team: Ruben Bach, Christoph Kern, Ashley Amaya, Prof. Dr. Frauke Kreuter, Prof. Fr. Florian Keusch, Jan Hecht and Jonathan Heinemann

Publications:  Amaya, A., Bach, R. L., Kreuter, F. und Keusch, F. (2020). Measuring the strength of attitudes in social media data. In Big data meets survey science : a collection of innovative methods (S. 163-192). https://doi.org/10.1002/9781118976357.ch5

Bach, R. L. und Wenz, A. (2020). Studying health-related internet and mobile device use using web logs and smartphone records. PLOS ONE, 15, e0234663. https://doi.org/10.1371/journal.pone.0234663

Bach, R. L., Kern, C., Amaya, A., Keusch, F., Kreuter, F., Hecht, J. und Heinemann, J. (2019). Predicting voting behavior using digital trace data. Social Science Computer Review : SSCORE. https://doi.org/10.1177/0894439319882896

Cernat, A. & Keusch, F. (2020). Do surveys change behaviour? Insights from digital trace data. International Journal of Social Research Methodology : IJSRM. https://doi.org/10.1080/13645579.2020.1853878

 International Program in Survey Practice and Data Science

The demand for well-trained experts in data collection and data analysis is rising, because not only private-sector companies but also governments and non-profit organizations increasingly use data from surveys for monitoring and decision-making. Employees in this sector usually have academic training in the social and economic sciences and some training in statistics, but no training in the job they are actually doing. Skills are acquired on the job.

The demand for well-trained experts in data collection and data analysis is rising, because not only private-sector companies but also governments and non-profit organizations increasingly use data from surveys for monitoring and decision-making. Employees in this sector usually have academic training in the social and economic sciences and some training in statistics, but no training in the job they are actually doing. Skills are acquired on the job.

The international state of the art in survey methodology and data science is often unknown. Because of rapid developments both in the practical field as well as in academic research, the gap between research and practice is wide. It is necessary to facilitate a faster transfer of knowledge in this area where change and refinement of data collection techniques and the opportunities to combine different data sources are ongoing. These developments also challenge data analysis techniques. At the same time, as more and more companies operate internationally and international markets grow, there is an increasing demand for practitioners sensitive to country-specific issues in data quality etc. The internationally oriented online professional studies is designed to lead to a Master's degree to fill this gap.

We are excited to announce that the International Program in Survey and Data Science is now offered as Mannheim Master of Applied Data Science at Mannheim Business School. You can join the IPSDS courses not only through the Master's program, but also as single open courses or as part of our certificates.

Project Team: Prof. Dr. Frauke Kreuter, Prof. Dr. Florian Keusch, Karin Frößinger, Markus Herklotz, Annika Spranz