Brief description

The inter­dis­ci­plin­ary re­search area From Prediction to Agile Interventions in the Social Sciences (FAIR) brings together researchers from the data sciences, statistics, education sciences, psychology, rehabilitation studies, and sociology. FAIR researchers from these different disciplines focus on the development and application of in­no­va­ti­vere­search methods from the data sciences and use them to address societal challenges in highly re­le­vant areas such as education, health, and societal inclusiveness and participation.

Increasingly larger and more complex data have become available in the social sciences that can contribute to more precise prediction models (e.g., for such outcomes as academic success, health, and well-being) and aid our understanding of cause-effect relationships. An im­por­tant objective of FAIR is the development of a frame­work for “Agile Intervention Research” that allows for individualized, data-driven, and need-based adaptations of interventions in authentic contexts. FAIR's researchers will use “big” (large data sets) and “small” (small case numbers) data to optimize prognostic models in the social sciences and maximize the impact of available interventions in these fields by allowing for individualized adaptations. 

Project term

2021-2024

Team of the TU Dort­mund Uni­ver­sity

Prof. Dr. Martina Brandt, Prof. Phillip Doebler, Prof. Dr. Markus Pauly, Prof. Fani Lauermann, Ph.D., Prof. Dr. Nele Mc Elvany, Prof. Dr. Tobias Kuhn, Prof. Dr. Sarah Weigelt, Dr. Alexander Munteanu, Prof Dr. Katja Ickstadt 

Funding

Ministry for Culture and Science of the State of North Rhine-Westphalia

Project website