Item Response Theory
eBook - PDF

Item Response Theory

Parameter Estimation Techniques, Second Edition

  1. 528 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Item Response Theory

Parameter Estimation Techniques, Second Edition

Book details
Table of contents
Citations

About This Book

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

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Yes, you can access Item Response Theory by Frank B. Baker, Seock-Ho Kim, Frank B. Baker, Seock-Ho Kim in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2004
ISBN
9781482276725
Edition
2
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Preface to the Second Edition
  7. Preface to the First Edition
  8. Contents
  9. 1. The Item Characteristic Curve: Dichotomous Response
  10. 2. Estimating the Parameters of an Item Characteristic Curve
  11. 3. Maximum Likelihood Estimation of Examinee Ability
  12. 4. Procedures for Estimating Both Ability and Item Parameters.
  13. 5. The Rasch Model
  14. 6. Parameter Estimation via MMLE and an EM Algorithm
  15. 7. Bayesian Parameter Estimation Procedures
  16. 8. The Graded Item Response
  17. 9. Nominally Scored Items
  18. 10. Parameter Estimation for Multiple Group Data
  19. 11. Estimation of Item Parameters of Mixed Models
  20. 12. Parameter Estimation via Gibbs Sampler
  21. A. Implementation of Maximum Likelihood Estimation of Item Parameters
  22. B. Implementation of Maximum Likelihood Estimation of Examinee's Ability
  23. C. Implementation of JMLE Procedure for the Rasch Model
  24. D. Implementation of Item Parameter Estimation via MMLE/EM
  25. E. Implementing The Bayesian Approach
  26. F. Implementation of Parameter Estimation Under the Graded Response Model
  27. G. Implementation of MLE Under Nominal Response Scoring
  28. H. Implementation of MMLE/EM for the Rasch Model
  29. I. Implementation of Multiple Groups Estimation
  30. J. Implementation of Estimation for Mixed Models
  31. K. Implementation of Gibbs Sampler
  32. References
  33. Index