本书提供了在项目反应理论中用到的经典和现代的统计工具。虽然项目反应理论十分依赖于统计工具来处理其模型和应用，但是系统地进行介绍或阐述与项目反应理论相关的统计学文献几乎很难找到，本书即填补了这一空白。 本书逻辑清晰、通俗易懂，共包含四个部分，具体内容有概率比与其他反应函数、多元正态分布、非全同随机变量和的分布、具有干扰和附带参数的模型、期望最大算法与推广、贝叶斯估计、变分近似法、残差分析的模型拟合、多维常态模型等。

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
编辑手记