Title Item Response Theory : Parameter Estimation Techniques, Second Edition / editors, Frank B. Baker, Seock-Ho Kim

Edition Second edition Published Boca Raton, FL : CRC Press, 2004

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Description 1 online resource : text file, PDF Series Statistics, textbooks and monographs ; 176 Statistics, textbooks and monographs ; v. 176. 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 Summary "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 estimation for a test with mixed item types, and Markov chain Monte Carlo methods. It includes discussions on issues related to statistical theory, numerical methods, and the mechanics of computer programs for parameter estimation, which help to build a clear understanding of the computational demands and challenges of IRT estimation procedures."--Provided by publisher Bibliography Includes bibliographical references (pages 465-481) and index Subject Psychology -- Methodology. Psychology -- Statistics Quantitative research. Mathematical statistics. Mathematical statistics Psychology Psychology -- Methodology Quantitative research Probabilistische Testtheorie Itemresponsietheorie. Genre/Form Statistics Form Electronic book Author Baker, Frank B., editor Kim, Seock-Ho, editor ISBN 9781482276725 1482276720 9780429153624 0429153627

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