Item Response Theory : Parameter Estimation Techniques /: Parameter Estimation Techniques. (2004)

• Record Type: Book
• Further Information: Note: Frank B. Baker, Seock-Ho Kim.
• Authors: Baker, Frank B; Kim, Seock-Ho
• Contents: Cover; 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 Curve2.1 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 Model3.2.3 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 Objectivity5.5 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 Likelihood6.3 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
• Edition: Second edition, revised and expanded
• Publisher Details: Boca Raton, FL : CRC Press
• Publication Date: 2004
• Extent: 1 online resource
• Subjects: 519.5/44; Psychology -- Methodology; Psychology -- Statistics; Quantitative research; Mathematical statistics; Mathematical statistics; Psychology; Psychology -- Methodology; Quantitative research; Electronic books; Statistics
• Languages: English
• ISBNs: 9781482276725; 1482276720
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.