Efnisyfirlit

• Half Title
• Title Page
• Copyright Page
• Dedication
• Preface to the Second Edition
• References
• Preface to the First Edition
• References
• Contents
• 1. The Item Characteristic Curve: Dichotomous Response
• 1.1 Introduction
• 1.2 The Item Characteristic Curve
• 1.3 Two Item Characteristic Curve Models
• 1.3.1 The Normal Ogive Model
• 1.3.2 The Logistic Ogive Model
• 1.4 Extension of the Item Characteristic Curve Models: Dichotomous Scoring
• 1.4.1 Birnbaum’s Three-paralneter Model
• 1.4.2 The One-parameter Logistic Model-The Rasch Model
• 1.5 Summary
• 2. Estimating the Parameters of an Item Characteristic Curve
• 2.1 Introduction
• 2.2 Maximum Likelihood Estimation: Normal Ogive Model
• 2.3 Maximum Likelihood Estimation: Logistic Model
• 2.4 Influence of the Weighting Coefficients
• 2.5 The Item Log-Likelihood Surface
• 2.6 Maximum Likelihood Estimation: Three-Parameter Model
• 2.7 Minimum X2 and Minimum Transform X2 Estimations
• 2.7.1 Minimum X2 Estimation
• 2.7.2 Minimum Transform X2 Estimation
• 2.8 Summary
• 3. Maximum Likelihood Estimation of Examinee Ability
• 3.1 Introduction
• 3.2 Maximum Likelihood Estimation of Ability
• 3.2.1 Normal Model
• 3.2.2 Logistic Model
• 3.2.3 Birnbaum’s Three-Parameter (Logistic) Model
• 3.3 Information Functions
• 3.3.1 Item Information Function
• 3.3.2 Samejima’s Approach to the Item Information Function
• 3.3.3 Test Information Function
• 3.4 Summary
• 4. Procedures for Estimating Both Ability and Item Parameters.
• 4.1 Introduction
• 4.2 Joint Maximum Likelihood Estimation: The Birnbaum Paradigm
• 4.2.1 Some Additional Facets of the Birnbaum Paradigm
• 4.2.2 Quality of the Parameter Estimates
• 4.3 Summary
• 5. The Rasch Model
• 5.1 Introduction
• 5.2 The Rasch Model
• 5.3 Separation of Parameters
• 5.4 Specific Objectivity
• 5.5 Conditional Maximum Likelihood Estimation Procedures
• 5.6 Application of the JMLE Procedure to the Rasch Model
• 5.6.1 Implementation of the JMLE Paradigm
• 5.6.2 Bias of the Parameter Estimates
• 5.7 Measuring the Goodness of Fit of the Rasch Model
• 5.7.1 Chi-square Tests for Goodness of Fit
• 5.7.2 Likelihood Ratio Tests for Goodness of Fit
• 5.8 The Rasch Model and Additive Conjoint Measurement
• 5.9 Research Related to the Rasch Model
• 5.10 Summary
• 6. Parameter Estimation via MMLE and an EM Algorithm
• 6.1 Introduction
• 6.2 Item Parameter Estimation via Marginal Maximum Likelihood
• 6.3 The Bock and Lieberman Solution
• 6.3.1 Quadrature Distributions
• 6.4 The Bock and Aitkin Solution
• 6.4.1 Some Background on the EM Algorithm
• 6.5 Summary
• 7. Bayesian Parameter Estimation Procedures
• 7.1 Introduction
• 7.2 The Bayesian Approach to Parameter Estimation
• 7.3 The Marginalized Bayesian Estimation Procedure
• 7.4 Marginalized Bayesian Item Parameter Estimationin PC-BILOG
• 7.4.1 The Likelihood Component
• 7.4.2 The Prior Distribution Component
• 7.4.3 Bayesian Modal Estimation via EM
• 7.5 Estimation of Ability
• 7.5.1 Bayesian Modal Estimation
• 7.5.2 Bayes EAP Estimation
• 7.6 Role of the Prior Distributions: Shrinkage
• 7.7 Research on Marginal Bayesian Parameter Estimation
• 7.8 Summary
• 8. The Graded Item Response
• 8.1 Introduction
• 8.2 Some Fundamentals of the Graded Response Case
• 8.3 Parameter Estimation Procedures for the Graded Response Case
• 8.3.1 Maximum Likelihood Estimation of the Item Parameters
• 8.3.2 Ability Estimation in the Graded Response Case
• 8.4 The Information Function for the Graded Response Case
• 8.4.1 Normal ICC Model and Graded Response Information
• 8.4.2 Logistic ICC Model and Graded Response Information
• 8.5 Other Models for Polytomous Items
• 8.6 Research on the Graded Response Model
• 8.7 Summary
• 9. Nominally Scored Items
• 9.1 Introduction
• 9.2 Maximum Likelihood Estimation of Item Parameters
• 9.3 Maximum Likelihood Estimation of Ability
• 9.4 The Information Functions
• 9.5 Extensions of Bock’s Nominal Response Model
• 9.6 The Relation of Nominally Scored Items and Logit-Linear Models
• 9.6.1 Maximum Likelihood Estimation of Item Parameters Under a Logit-Linear Approach
• 9.7 Summary
• 10. Parameter Estimation for Multiple Group Data
• 10.1 Introduction
• 10.2 Estimating Properties of Distributions
• 10.3 Estimation of a Latent Distribution
• 10.4 The Multiple Group Model
• 10.5 Parameter Estimation
• 10.6 Summary
• 11. Estimation of Item Parameters of Mixed Models
• 11.1 Introduction
• 11.2 A General Model
• 11.3 Parameter Estimation via Marginal Maximum Likelihood
• 11.3.1 Estimation of Item Parameters
• 11.3.2 Estimation of Ability Parameters
• 11.4 Research on Mixed Item Types
• 11.5 Summary
• 12. Parameter Estimation via Gibbs Sampler
• 12.1 Introduction
• 12.2 Albert’s Gibbs Sampler
• 12.2.1 The Logic of Gibbs Sampler
• 12.2.2 Albert’s Implementation of Gibbs Sampler
• 12.2.3 Continuing the Markov Chain
• 12.3 Gibbs Sampler: Another Approach
• 12.3.1 Gibbs Sampler for Item Response Models
• 12.3.2 Steps of Gibbs Sampler
• 12.3.3 Model Specifications
• 12.3.4 Starting Values
• 12.3.5 Output Monitoring
• 12.3.6 Summary Statistics
• 12.4 Extensions of Gibbs Sampler within Item Response Theory
• 12.5 Empirical Studies of Gibbs Sampler
• 12.6 Summary
• A. Implementation of Maximum Likelihood Estimation of Item Parameters
• A.1 Introduction
• A.2 Implementation
• A.3 BASIC Computer Program
• B. Implementation of Maximum Likelihood Estimation of Examinee’s Ability
• B.1 Introduction
• B.2 Implementation
• B.3 BASIC Computer Program
• C. Implementation of JMLE Procedure for the Rasch Model
• C.1 Introduction
• C.2 Implementation
• C.3 BASIC Program
• D. Implementation of Item Parameter Estimation via MMLE/EM
• D.1 Introduction
• D.2 Implementation
• D.3 BASIC Computer Program
• E. Implementing The Bayesian Approach
• E.1 Introduction
• E.2 Marginal Bayesian Modal Item Parameter Estimation
• E.3 Implementation of Marginalized Bayesian Modal Item Parameter Estimation
• E.4 Implementation of Bayesian Estimation of an Examinee’s Ability
• E.4.1 Bayesian Modal Estimation
• E.4.2 Bayesian “Expected A Posteriori” Estimation
• E.5 BASIC Computer Programs
• E.5.1 Program for Marginalized Bayesian Item Parameter Estimation
• E.5.2 Program for Bayesian Modal Estimation of an Examinee’s Ability
• E.5.3 Program for Bayesian Expected A Posteriori Estimation of an Examinee’s Ability
• F. Implementation of Parameter Estimation Under the Graded Response Model
• F.1 Introduction
• F.2 Implementation of Item Parameter Estimation
• F.3 Implementation of Ability Estimation
• F.4 BASIC Computer Programs
• F.4.1 Item Parameter Estimation
• F.4.2 Ability Estimation
• G. Implementation of MLE Under Nominal Response Scoring
• G.1 Introduction
• G.2 Implementation of Item Parameter Estimation
• G.3 Implementation of Ability Estimation
• G.3.1 Introduction
• G.3.2 Implementation of Ability Estimation
• G.4 BASIC Computer Programs
• G.4.1 Item Parameter Estimation
• G.4.2 Ability Estimation
• H. Implementation of MMLE/EM for the Rasch Model
• H.1 Introduction
• H.2 Implementation
• H.3 BASIC Computer Programs
• H.3.1 Marginal Maximum Likelihood Estimation for the Rasch Model
• H.3.2 Marginal Maximum Likelihood Estimation for the Rasch Model: Simpler Relaxation Solution
• I. Implementation of Multiple Groups Estimation
• I.1 Introduction
• I.2 Estimation of Latent Distribution Parameters
• I.3 Estimation of Both Item and Latent Distribution Parameters
• I.4 BASIC Computer Programs
• I.4.1 Estimation of Latent Distribution Parameters
• I.4.2 Estimation of Both Item and Latent Distribution Parameters: The Compact Model
• I.4.3 Estimation of Both Item and Latent Distribution Parameters: The Augmented Model
• J. Implementation of Estimation for Mixed Models
• J.1 Introduction
• J.2 Implementation
• J.3 BASIC Computer Program
• K. Implementation of Gibbs Sampler
• K.1 Introduction
• K.2 Implementation
• K.3 BASIC Computer Program
• References
• Index