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.
The International Cognitive Ability Resource (ICAR)
Project lead: Philipp Doebler together with Heinz Holling (Münster), John Rust (Cambridge, UK) and William Revelle (Evanston, US)
Funded from 2014 to 2019 by the DFG, ESRC and NSF in the context of the Open Research Area in the Social Sciences (ORA).
The International Cognitive Ability Resource (ICAR) project led to the creation of a large collection of assessment instruments for cognitive abilities that can be used in a wide range of applications. Several hundred research groups around the world use the assessment instruments developed through this initiative. In addition to the core team, other groups have contributed—and continue to contribute—to the ongoing revision and further development of these instruments. ICAR makes use of automatic item generation techniques, which produce test items with predictable psychometric properties. More details about ICAR can be found on the project's dedicated information page.
The International Cognitive Ability Resource for Large-Scale International Mobile Applications
Funded from 2020 to 2021 by The Eric and Wendy Schmidt Fund for Strategic Innovation.
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, 75(3), 411-443. https://doi.org/10.1111/bmsp.12273
- Beisemann M, Holling H, Doebler P. (2025). Every Trait Counts: Marginal Maximum Likelihood Estimation for Novel Multidimensional Count Data Item Response Models with Rotation or l1–Regularization for Simple Structure. Psychometrika. 90(1), 304-330. https://doi.org/10.1017/psy.2024.17
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)