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Contents for Models Contents for Applications Preface Contributors Section ¢ñ Basic Tools 1.Logit, Probit, and Other Response Functions James H. Albert 2.Discrete Distributions Jodi M. Casabianca and Brian W. Junker 3.Multivariate Normal Distribution Jodi M. Casabianca and Brian W. Junker 4.Exponential Family Distributions Relevant to IRT Shelby J. Haberman 5.Loglinear Models for Observed-Score Distributions Tim Moses 6.Distributions of Sums of Nonidentical Random Variables . Wire J. van der Linden 7.Information Theory and Its Application to Testing Hua-Hua Chang, Chun Wang, and Zhiliang Ying Section ¢ò Modeling Issues 8.Identification of Item Response Theory Models Ernesto San Martin 9.Models with Nuisance and Incidental Parameters Shelby J. Haberman 10. Missing Responses in Item Response Modeling Robert J. Mislevy Section ¢ó Parameter Estimation 11. Maximum-Likelihood Estimation Cees A. W. Glas 12. Expectation Maximization Algorithm and Extensions Murray Aitkin 13. Bayesian Estimation Matthew S. Johnson and Sandip Sinharay 14. Variational Approximation Methods Frank Rijmen, Minjeong Jeon, and Sophia Rabe-Hesketh 15. Markov Chain Monte Carlo for Item Response Models... Brian W. Junker, Richard J. Patz, and Nathan M. VanHoudnos 16. Statistical Optimal Design Theory Heinz Holling and Rainer Schwabe Section ¢ô Model Fit and Comparison 17. Frequentist Model-Fit Tests Cees A. W. Glas 18. Information Criteria Allan S. Cohen and Sun-Joo Cho 19. Bayesian Model Fit and Model Comparison Sandip Sinharay 20. Model Fit with Residual Analyses Craig S. Wells and Ronald K. Hambleton Index ±à¼­ÊÖ¼Ç