EDMS 738—Special Topics in Measurement

Prof. Mislevy, Fall 2007

Bayesian inference and measurement models

This course begins with an overview of the concepts and methods of Bayesian inference, with a particular emphasis on educational and psychological measurement models.  Issues of estimation for individual students and population structures will be addressed.  The methods on which attention will be focused are discrete Bayesian inference networks and  Markov Chain Monte Carlo (MCMC) estimation.  The models we will address include classical test theory, item response theory, latent class models, factor analysis, and cognitive diagnosis.  The prerequisites are statistics up through regression and correlation, an introductory measurement course, and further work with at least one of the types of psychometric models listed above.  The text for the course is

Gelman, Carlin, Stern, & Rubin. Bayesian data analysis (2nd ed.).

Drafts of chapters will also be provided from three books in progress:

Almond, Mislevy, Williamson, Yan, & Steinberg. Bayes nets in education assessment.

Mislevy, Mazzeo, Lim, & Kulick. Design, analysis, and reporting in large-scale assessment.

Levy, R. Bayesian inference and measurement models.