Applied Uni- and Multidimensional IRT Analysis in R

Users from all professional groups who have basic knowledge in statistics and have used R before. (For introductory R courses please revisit the course list.)

• CHF 600 for members of UZH/ETH and associated institutes
• CHF 800 for members of other universities, federal and cantonal research facilities and agencies, non-profit organisations etc.
• CHF 1,200 for companies, banks, insurance companies etc.

Item response theory (IRT) is a latent variable framework designed for modeling educational and psychological measurements. Popular representations of IRT include the Rasch, partial credit, graded response, and three-parameter logistic model. Today, IRT has become the state of the art in a variety of scientific disciplines ranging from large scale educational assessments like the PISA study and standardized psychological testing to the development of objective scales for measuring, e.g, the degree of physical impairment in medical research.

The outline of the course includes:

• overview of basic and advanced IRT concepts useful for applied item analyses
• statistical estimation techniques for obtaining optimal parameters estimates in unidimensional and multidimensional models
• hands-on applied analysis with mirt, including model fit and item diagnostic utilities, fitting common and customize item response models, nonparametric estimation of latent traits, and so on
• modeling explanatory fixed and random item- and person-covariates to explain test variation
• utilizing multiple-group estimation techniques to detect differential item and test functioning
• overview of test scoring methods, and demonstrate how these scoring methods integrate with computerized adaptive testing (CAT) applications
• hands-on creation of real-time (and off-line) CAT and multidimensional CATs with the mirtCAT package
• and more.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.