eBook - PDF
Item Response Theory
Parameter Estimation Techniques, Second Edition
- 528 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
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 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
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Preface to the Second Edition
- Preface to the First Edition
- Contents
- 1. The Item Characteristic Curve: Dichotomous Response
- 2. Estimating the Parameters of an Item Characteristic Curve
- 3. Maximum Likelihood Estimation of Examinee Ability
- 4. Procedures for Estimating Both Ability and Item Parameters.
- 5. The Rasch Model
- 6. Parameter Estimation via MMLE and an EM Algorithm
- 7. Bayesian Parameter Estimation Procedures
- 8. The Graded Item Response
- 9. Nominally Scored Items
- 10. Parameter Estimation for Multiple Group Data
- 11. Estimation of Item Parameters of Mixed Models
- 12. Parameter Estimation via Gibbs Sampler
- A. Implementation of Maximum Likelihood Estimation of Item Parameters
- B. Implementation of Maximum Likelihood Estimation of Examinee's Ability
- C. Implementation of JMLE Procedure for the Rasch Model
- D. Implementation of Item Parameter Estimation via MMLE/EM
- E. Implementing The Bayesian Approach
- F. Implementation of Parameter Estimation Under the Graded Response Model
- G. Implementation of MLE Under Nominal Response Scoring
- H. Implementation of MMLE/EM for the Rasch Model
- I. Implementation of Multiple Groups Estimation
- J. Implementation of Estimation for Mixed Models
- K. Implementation of Gibbs Sampler
- References
- Index