Methods to Account for Subject-Covariates in IRT-Models

DFG project, conducted at the LMU Munich

Principal investigator Prof. Dr. Carolin Strobl
Staff Basil Abou El-Komboz
Duration of the project December 1, 2009, to November 30, 2012
Funded by German Research Foundation (DFG)


Item Response Theory (IRT) comprises a variety of statistical models for linking the latent traits of subjects to their reactions to test items or stimuli. One example is the application of the Rasch model to measure latent abilities, where the parameters of all persons and items are represented on a common scale. Since, however, a common scale can often not be assumed for different groups of subjects, several methods - including latent-class approaches - have been suggested for incorporating observable subject covariates into the model. The existing approaches have some drawbacks, though: In many cases the information available in the covariates is not fully utilized. Moreover, complex parametric models are hard to interpret for the vast majority of applied scientists. Therefore, the aim of this research project is to develop a flexible and yet easy-to-handle range of methods that allows to incorporate subject covariates of all kinds - alone and in combination with latent-class approaches - into a variety of IRT models. Application areas of these methods in psychology and empirical education research include the exploratory modeling of heterogeneity as well as the hypothesis-driven application as a test for the validity of a common model.


Publications

  • Eugster, M., F. Leisch, and C. Strobl (2013). (Psycho-)analysis of benchmark experiments - A formal framework for investigating the relationship between data sets and learning algorithms. Computational Statistics & Data Analysis. (Accepted.).
  • Strobl, C., J. Kopf, and A. Zeileis (2013). Rasch trees: A new method for detecting differential item functioning in the Rasch model. Psychometrika. (In press.).
  • Frick, H., C. Strobl, F. Leisch, and A. Zeileis (2012). Flexible Rasch mixture models with package psychomix. Journal of Statistical Software 48(7), 1-25.
  • Strobl, C., F. Wickelmaier, and A. Zeileis (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics 36(2), 135-153.
  • Strobl, C., J. Kopf, and A. Zeileis (2010). Wissen Frauen weniger oder nur das Falsche? - Ein statistisches Modell für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben. In S. Trepte and M. Verbeet (Eds.), Allgemeinbildung in Deutschland - Erkenntnisse aus dem SPIEGEL Studentenpisa-Test, pp. 255-272. Wiesbaden: VS Verlag.