From Prediction to Agile Interventions in the Social Sciences
Funded by MKW NRW as part of MKW-Profilbildung 2020
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 innovative research 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.
DFG-Project “The International Cognitive Ability Resource” (ICAR), since 2014
The International Cognitive Ability Resource is a public-domain assessment tool which aims to encourage the broader assessment of cognitive abilities in psychology and other social sciences and facilitate neuropsychological assessment in medical research and practice. More than 100 qualified research groups from all over the world already use the instruments we developed and contribute to their ongoing revision and further development. The ICAR makes use of automatic item generation which yields test items with predictable psychometric properties. Find more on this technique and the possibility to use ICAR-items for your own research on the project's homepage
Projective item response theory models for count data and their application as interpretable approximations to black-box machine learning models
Funded by the German Research Foundation (DFG)
Compared to IRT methods for binary data, count data IRT models are underdeveloped. Targeted further method development for count data allows to include unstructured indicators for latent variables, linking IRT more closely to machine learning methods. The research project develops projective IRT models for count data. In particular, projections can be made onto principal dimensions of commonly used existing models. The projective models find use as well-interpretable models in situations where a black-box machine learning model is used for its predictive or classification goodness. This results in approximations to black-box models that help to better understand them. Since multi- and even high-dimensional latent variable constellations are numerically expensive, an EM algorithm for a general count data IRT model is also developed.
Publications
- Beisemann, M. (2022). A flexible approach to modelling over‐, under‐and equidispersed count data in IRT: The Two‐Parameter Conway–Maxwell–Poisson Model. British Journal of Mathematical and Statistical Psychology. (Advance online publication.) https://doi.org/10.1111/bmsp.12273
Educational Progress of Children at Risk of Low Academic Achievement: A Psychometrically Informed Investigation of Educational Outcomes and Resilience Factors with Longitudinal Data of the National Educational Panel Study
Funded by the German Research Foundation (DFG) as part of the DFG Priority Programme Education as a Lifelong Process (SPP 1646)