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First edition
Boca Raton, FL : Chapman and Hall/CRC, [2017]
1 online zdroj : ilustrace
Externí odkaz    Plný text PDF 
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ISBN 9781315373645 (e-kniha : PDF)
ISBN 9781466514324 (print)
Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT) While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods..
* item response theory. * Monte Carlo * methods for dealing with missing data * test theory
Contents for Models ...ix // Contents for Applications ...xiii // Preface ...xvii // Contributors ...xxi // Section I Basic Tools // 1. Logit, Probit, and Other Response Functions ...3 - James H. Albert // 2. Discrete Distributions ...23 - Jodi M. Casabianca and Brian W. Junker // 3. Multivariate Normal Distribution ...35 - Jodi M. Casabianca and Brian W. Junker // 4. Exponential Family Distributions Relevant to IRT ...47 - Shelby J. Haberman // 5. Loglinear Models for Observed-Score Distributions ...71 - Tim Moses // 6. Distributions of Sums of Nonidentical Random Variables ...87 - Wim J. van der Linden // 7. Information Theory and Its Application to Testing ...105 - Hua-Hua Chang, Chun Wang, and Zhiliang Ying // Section II Modeling Issues // 8. Identification of Item Response Theory Models ...127 - Ernesto San Martin // 9. Models with Nuisance and Incidental Parameters ...151 - Shelby J. Haberman // 10. Missing Responses in Item Response Modeling ...171 - 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

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