Title: Bayesian Item Response Modeling in R with brms and Stan

Author: Paul-Christian Bürkner

Affiliation: Cluster of Excellence SimTech, University of Stuttgart

Abstract:
Item response theory (IRT) is widely applied in the human sciences to model
persons' responses on a set of items measuring one or more latent constructs.
While several software packages have been developed that implement IRT models,
they tend to be restricted to respective prespecified classes of models.
Further, most implementations are frequentist while the availability of Bayesian
methods remains comparably limited. In this talk, I demonstrate how to use the R
package brms together with the probabilistic programming language Stan to
specify and fit a wide range of Bayesian IRT models using flexible and intuitive
multilevel formula syntax. Various distributions for categorical, ordinal, and
continuous responses are supported. Users may even define and apply their own
custom response distributions. In multiple real-world examples, I will
illustrate the specification and post-processing of IRT models in the new
framework.