Publications & Resources
Modern Approaches to Parameter Estimation in Item Response Theory
Li Cai and David Thissen
Entire volumes (e.g., Baker & Kim, 2004) have been dedicated to the discussion of statistical parameter estimation techniques for item response theory (IRT) models. There has also been much recent development in the technical literature on improved methods for estimating complex IRT models (e.g., Cai, 2010a, 2010b; Edwards, 2010; Rijmen & Jeon, 2013). We offer here a map to help researchers and graduate students understand the fundamental challenges of IRT parameter estimation, and appropriately contextualize the underlying logic of some of the proposed solutions. We assume that the reader is familiar with elementary probability concepts such as prior, posterior, and likelihood, as well as the equations for describing statistical models for categorical observed data, for example, logistic regression. For methodologically inclined readers interested in studying IRT parameter estimation and in trying out some of the approaches discussed here, the combination of conceptual sections and more technical sections should be sufficient as a basis of software implementation.
Cai, L., & Thissen, D. (2014). Modern approaches to parameter estimation in item response theory. In S. P. Reise & D. A. Revicki (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 41-59). New York, NY: Routledge.