Item Response Theory clearly describes the most recently developed IRT models and furnishes detailed explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models. Extensively revised and expanded, this edition offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods. It includes discussions on issues related to statistical theory, numerical methods, and the mechanics of computer programs for parameter estimation, which help to build a clear understanding of the computational demands and challenges of IRT estimation procedures.
"…an excellent resource for the serious investigator doing research involving estimation of IRT model parameters."
-Journal of the American Statistical Association
"…Baker has the unique ability to present complex material in a form that is easily understood….This book belongs on the bookshelf of every advanced student in psychometrics. It should also prove invaluable to students in statistics."
-Journal of Educational Measurement
The Item Characteristic Curve: Dichotomous Response
Estimating the Parameters of an Item Characteristic Curve
Maximum Likelihood Estimation of Examinee Ability
Maximum Likelihood Procedures for Estimating Both Ability and Item Parameters
The Rasch Model
Marginal Maximum Likelihood Estimation and an EM Algorithm
Bayesian Parameter Estimation Procedures
The Graded Item Response
Nominally Scored Items
Markov Chain Monte Carlo Methods
Parameter Estimation with Multiple Groups
Parameter Estimation for a Test with Mixed Item Types