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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

  • Watteler, Oliver, Constantin Hammer, and Heidi Schuster. 2024. "'Berechtigtes Interesse' als Rechtsgrundlage für die Erhebung und Archivierung von Social Media Daten." Nachhaltige Archivierung sozialer Medien - Twitter und danach, Deutsche Nationalbibliothek, Frankfurt a.M., 2024-03-20. doi: https://doi.org/10.5281/zenodo.11030035.
  • Quandt, Markus, and Peter Schmidt. 2024. "Introduction to the special issue of the International Journal of Comparative Sociology on “National identity, nationalism, patriotism, and globalization”." International Journal of Comparative Sociology 2024 (65, 2): 101-111. doi: https://doi.org/10.1177/00207152241232577.
  • Behr, Dorothée, Michael Braun, and Luisa Aiglstorfer. 2024. "Showcasing the usefulness of web probing: Do Subtle Variations in Questionnaire Translation Lead to Different Survey Responding?" Quality & Quantity online first. doi: https://doi.org/10.1007/s11135-024-01843-8.
  • Singh, Ranjit K., Cornelia Neuert, and Tenko Raykov. 2023. "Assessing conceptual comparability of single-item survey instruments with a mixed-methods approach." Quality & Quantity online first. doi: https://doi.org/10.1007/s11135-023-01801-w.
  • Hadler, Patricia. 2023. Context effects in question evaluation via web probing: Exploring the interaction of open-ended and closed survey questions. Mannheim: MADOC. https://madoc.bib.uni-mannheim.de/66207/.
  • Sack, Harald, Torsten Schrade, Oleksandra Bruns, Etienne Posthumus, Tabea Tietz, Ebrahim Norouzi, Jörg Waitelonis, Heike Fliegl, Linnaea Söhn, Julia Tolksdorf, Jonatan Jalle Steller, Abril Az´ocar Guzm´an, Said Fathalla, Ahmad Zainul Ihsan, Volker Hofmann, Stefan Sandfeld, Felix Fritzen, Amir Laadhar, Sonja Schimmler, and Peter Mutschke. 2023. "Knowledge Graph Based RDM Solutions : NFDI4Culture - NFDI-MatWerk - NFDI4DataScience ." In 1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities , edited by York Sure-Vetter, and Carole Globe, doi: https://doi.org/10.52825/CoRDI.v1i.371.
  • Otto, Wolfgang, Matthäus Zloch, Lu Gan, Dr. Saurav Karmakar, and Stefan Dietze. 2023. "GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets." In Findings of the Association for Computational Linguistics: EMNLP 2023, edited by Houda Bouamor, Juan Pino, and Kalika Bali, 8166-8176. Singapore: Association for Computational Linguistics. https://aclanthology.org/2023.findings-emnlp.548.
  • Schoch, David, and Termeh Shafie. 2024. "The interplay of structural features and observed dissimilarities among centrality indices." Social Networks 78 (July 2024): 54-64. doi: https://doi.org/10.1016/j.socnet.2023.11.006.
  • Mutschke, Peter. 2023. "Zentralitäts- und Prestigemaße." online first. In Handbuch Netzwerkforschung, edited by Christian Stegbauer, and Roger Häußling, Netzwerkforschung. Wiesbaden: Springer VS. doi: https://doi.org/10.1007/978-3-658-37507-2_33-1.
  • Sen, Indira, Dennis Assenmacher, Mattia Samory, Isabelle Augenstein, Wil van der Aalst, and Claudia Wagner. 2023. "People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection." 2023. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP, edited by Houda Bouamor, Juan Pino, and Kalika Bali, 10480-10504. Singapore: Association for Computational Linguistics.

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.