The Department of Statistics (Faculty 5) is responsible for profile projects in the area of method development. Postdocs develop flexible quantitative methods with improved predictive accuracy for large data sets (large-scale studies; panel data; digital behavioral traces) and small case numbers (single-case/few-case research designs; heterogeneous (intervention) effects in small subgroups, repeated measures in ABAB designs and within-person RCTs). In addition, there are techniques that combine these areas and thus can quantify causal effects of agile interventions. Through methodological research, participation in data (re)analysis and prognostic modeling, as well as study design, the department is the scientific liaison for the FAIR profile area.

The Center for Research on Education and School Development (Institut für Schulentwicklungsforschung, IFS) at the Faculty for Educational Science, Psychology, and Educational Research is responsible for research projects in FAIR that will focus on the topic of education and on predictors of academic success. Our research in FAIR will focus in particular on the development and application of prediction and intervention models that can help us to better understand what factors predict academic success in different educational contexts (e.g., in schools and higher education). We will use data from large-scale assessments such as PIRLS/IGLU and NEPS, as well as new data (to be collected over the next three years) from intervention studies using between- and within-person randomized controlled trials. Jointly with the other research teams in FAIR, we will develop a framework for “Agile Intervention Research” that allows for individualized and need-based adaptations of interventions and learning support systems in authentic educational contexts. An important goal is also the analysis of heterogeneous intervention effects. Our planned interventions will focus on such topics as learning strategies designed to foster reading comprehension and deep-level understanding, the development of reading and math competence, and educational and career choices.

The Chair of Social Structure and Sociology of Ageing Societies at the Department of Social Sciences conducts empirical research on age(ing), in particular analyses of cumulative inequalities across the life course (work, family, health) and wellbeing in older age based on regionally and internationally comparative longitudinal data. The analysis of such macro-micro links and trajectories serves to identify causalities and thus, not least, to the social science-based development of evidence-based social policy measures to promote wellbeing in times of rapid demographic change. In addition, process data of adaptive assistance systems as well as interventions in the field of care and health services are analysed and used as a basis for the development of agile, target group-specific intervention approaches. Analogous to the other fields, the handling of complex data sets (large-scale and intensive process data) and small case numbers (e.g., for heterogeneous intervention effects) is crucial to develop valid prognostic models as well as tailored, agile, target group-specific interventions for healthy ageing.

In the Department of Rehabilitation Sciences, the focus of FAIR lies on analysing data relating to a computer-based intervention for children with learning disorders (large-scale data). Further, we conduct small-sample studies pertaining to individually adaptive prevention and intervention measures relating to mathematics and vision. Here, a core concern in FAIR is the data-driven development of a tablet-based, adaptive intervention for primary school-aged children with mathematical difficulties taking into account their visual capabilities. Additional measures will consider increased screen time and its potential influence on school performance as well as the development of vision.

The Dortmund Data Science Center (DoDSc) is an interdisciplinary research center that integrates long-standing expertises in handling large, high-dimensional data and in Bayesian statistics. These methods enable an efficient processing of large data streams and distributed data, and statistical analysis in presence of small case numbers.

The DoDSc provides method development for FAIR in the area of advanced data processing: for instance, scalable sketching and subsampling techniques provide interpretable aggregates of behavioral traces adaptively and in real time, and complex data streams of agile interventions can be analyzed using streamed statistical models.

In addition, the DoDSc is responsible within FAIR for the conception, organization, and implementation of modular qualification programs in the fields of quantitative methodological training for handling large data sets or small case numbers within social science oriented research questions.