目錄
Contents for Models
Contents for Applications
Preface
Contributors
Section Ⅰ 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 Ⅱ 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 Ⅲ 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 Ⅳ 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
編輯手記
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