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Research

GESIS - for a research-based infrastructure

GESIS is a research-based infrastructure institution for the social sciences and conducts its own continuous and interdisciplinary research in four major research areas. The results of our research serve both to gain scientific knowledge and to sustainably improve our offerings for the social sciences.

For GESIS, the quality of data takes center stage. GESIS strives to provide high-quality research data as well as methods and tools that enable users to assess for themselves how high the quality of research data is. The research focus is therefore also geared towards this core interest. In order to contribute to the generation of knowledge about data quality, GESIS focuses on the research areas of survey methodology, computer-based methods and research data management. Together, we focus our methodologically oriented research on supporting researchers who work with quantitative data.

Research output at GESIS

  • Zapilko, Benjamin, and York Sure-Vetter. 2013. "Neue Möglichkeiten für die Wissensorganisation durch die Kombination von Digital Library Verfahren mit Standards des Semantic Web." In Wissen – Wissenschaft – Organisation : 19. bis 21. Oktober 2009 ; Proceedings der 12. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, edited by Heinz-Peter Ohly, Fortschritte in der Wissensorganisation ; Bd. 12, 173-180. Würzburg: Ergon-Verl..
  • Zapilko, Benjamin, and Maximilian Stempfhuber. 2013. "Ein Ebenenmodell für die semantische Integration von Primärdaten und Publikationen in Digitalen Bibliotheken." In Wissen – Wissenschaft – Organisation : 19. bis 21. Oktober 2009 ; Proceedings der 12. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, edited by Heinz-Peter Ohly, Fortschritte in der Wissensorganisation ; Bd. 12, 121-130. Würzburg: Ergon-Verl..
  • Mutschke, Peter. 2010. "Zentralitäts- und Prestigemaße." In Handbuch Netzwerkforschung, edited by Christian Stegbauer, and Roger Häußling, 365-378. Wiesbaden: VS Verl. für Sozialwiss..
  • Mayr, Philipp, Peter Mutschke, Philipp Schaer, and York Sure-Vetter. 2013. "Mehrwertdienste für das Information Retrieval: das Projekt IRM." In Wissen – Wissenschaft – Organisation : 19. bis 21. Oktober 2009 ; Proceedings der 12. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, edited by Heinz-Peter Ohly, Fortschritte in der Wissensorganisation ; Bd. 12, 131–139. Würzburg: Ergon-Verl.. https://github.com/PhilippMayr/MyPapers/blob/master/texts/ISKO2009.pdf.
  • Lipinsky, Anke. 2012. "Transitioning to professorship - career decisions on mobility at the postdoc level." In The scientific and technological careers for women and men : private temporalities, professional temporalities; public and corporate policies, edited by André Béraud, and Yvonne Pourrat, 137-147. Paris: Sense Publ..
  • Ell, Theresia, Lydia Repke, and Henning Silber. 2024. "Personal and Technology-Based Communication and Its Impact on Mental Health From a Network Perspective." Sunbelt Conference 2024, Heriot-Watt University, Edinburgh, 2024-06-24.
  • Repke, Lydia, Theresia Ell, and Henning Silber. 2024. "Beyond Distancing - An Examination of Social Networks and Mental Health in the Covid-19 Era." Sunbelt Conference 2024, Heriot-Watt University, Edinburgh, 2024-06-24.
  • Abdedaiem, Amin, Abdelhalim Hafedh Dahou, Mohamed Amine Cheragui, and Brigitte Mathiak. 2024 (Forthcoming). "FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis." In ACLing 2024: 6th International Conference on AI in Computational Linguistics, Procedia Computer Science.
  • Dahou, Abdelhalim Hafedh, Mohamed Amine Cheragui, Amin Abdedaiem, and Brigitte Mathiak. 2024 (Forthcoming). "Enhancing Model Performance through Translation-based Data Augmentation in the context of Fake News Detection." In ACLing 2024: 6th International Conference on AI in Computational Linguistics., Procedia Computer Science.
  • Volle, Jonas, Andreas Schmitz, Haiko Lietz, and Richard Münch. 2024. "Group formation in science between homogenization and differentiation: Modeling the development of U.S. and German sociology." International Journal of Sociology online first. doi: https://doi.org/10.1080/00207659.2024.2357908.

Ein wesentliches Merkmal von GESIS ist, dass das Institut insbesondere bei den Daten, für die es auch die Erhebung verantwortet, sehr hohe Ansprüche und Standards an die Qualität der bereitge- stellten Daten anlegt. Daher ist es für GESIS zentral, eigene Beiträge zur Untersuchung und Verbes- serung von Aspekten der Datenqualität zu leisten. Die Forschung in den GESIS-Forschungsbereichen trägt deshalb direkt zum Schwerpunkt Datenqualität bei. Dies betrifft sowohl Umfragedaten als auch digitale Verhaltensdaten und relevante Metadaten. Datenqualität umfasst Aspekte der (a) Kor- rektheit und Repräsentativität von Daten und (b) Nutzbarkeit und FAIRness von Daten. Beispiele für (a) sind die Vollständigkeit, Korrektheit, Provenienz der Repräsentativität von Daten, während (b) Aspekte wie Findbarkeit, Qualität der Dokumentation, Aufbereitung oder die Interoperabilität von Daten und Metadaten berücksichtigt. Damit wird eine wichtige Voraussetzung dafür erfüllt, dass die Bearbeitung inhaltlicher Fragestel- lungen (Substantive Research) auf Basis dieser Daten zu validen Ergebnissen führt.

At GESIS we conduct basic and applied research in the field of survey methodology. Our survey research is divided into the focus areas of Survey Statistics, Survey Instruments, Survey Operations and Comparative Surveys. We pursue the goal of gaining evidence-based insights into how surveys and their data quality can be optimised. Within the framework of systematic reviews and meta-analyses, we evaluate existing research and identify research gaps. In the research area of survey methodology, we also explore the connection of survey data with digital behavioural data (e.g. social media profiles, smartphone usage data or browsing histories) and examine how these data types can be complemented and combined. To this end, we are also driving the transfer of established concepts for assessing the data quality of surveys to digital behavioural data.

In order to ensure a high level of quality of GESIS digital products and services in view of the rapid changes in information and knowledge technologies, GESIS conducts research in the field of applied informatics and information science.

The aim of this research area is to test, analyse, adapt, further develop and evaluate novel methods, models and algorithms of computer science in the application field of social sciences. A core component of this research area is, above all, the development of digital behavioural data such as data from social media or data generated by sensors for social science research. This is because the development and evaluation of methods for collecting, processing and analysing this new data expands the basis for answering social science questions. By implementing the knowledge gained, innovative and integrated research infrastructures and services tailored to the social sciences can thus be created in the future for all phases of the research data cycle.

Against the backdrop of the large and growing data base of GESIS, as well as the related offers for data reference and data archiving, research in this area is an important component for the expansion and progress of the related infrastructures.

Research in this field is concerned with long time preservation, data documentation and the legal framework of data access and licensing of data. Topics in this research area address the challenges arising from data sharing and data security. Additional important research topics are the creation of data documentation standards and meta data, the handling of new data types such as digital behavioral data and long time preservation issues.

Our commitment to diversified topics in the political and social sciences also ensures that we remain relevant and attentive to the latest trends and developments, thereby enriching our infrastructure offerings. We increase the visibility of our data through our publications and by presenting our research at relevant conferences. We show our data’s potential and their usability and promote the exchange with the relevant scientific communities.

Of particular importance is the application of current analytical models to the data such as cross-classified multilevel models and various applications of random, fixed and hybrid longitudinal models.