Research

The chair for Psychological Methods, Evaluation, and Statistics conducts methodological research at the intersection of statistics, psychometrics and machine learning. 

For example, we develop methods for detecting parameter differences in psychometric models, because these can violate the fairness or psychological tests. Some of these methods are based on classical, parametric statistics, others on approaches from machine learning.

In machine learning, we develop methods for assessing stability and for measuring and interpreting the contributions of predictor variables in a reliable way. These topics are also relevant to some of our work in psychometric computing.

In all research areas we contribute to the development of add-on packages for the free open-source software R. These R packages are listed below.

Application of Artificial Intelligence for Test Generation and Test Evaluation in Educational Assessments

For further topics see Publications

SNF project
Development of a Toolbox for Psychological Test Development

SNF project
Detecting Heterogeneity in Complex IRT Models for Measuring Latent Traits

BMBF project
Heterogeneity in IRT Models

DFG project
Methods to Account for Subject-Covariates in IRT-Models

 

BRIDGE Discovery project
Harnessing event and longitudinal data in industry and health sector through privacy preserving technologies

SNF Sinergia project
MULTICAST- A MULTIdisCiplinary Approach to prediction and treatment of Suicidality

DSI project
PREMIA - A Prediction Market with Integrated Algorithms