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The Theory and Practice of Item Response Theory

The Theory and Practice of Item Response Theory

          
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About the Book

This book has been replaced by The Theory and Practice of Item Response Theory, Second Edition, ISBN 978-1-4625-4775-3.

Table of Contents:
Symbols and Acronyms 1. Introduction to Measurement - Measurement - Some Measurement Issues - Item Response Theory - Classical Test Theory - Latent Class Analysis - Summary 2. The One-Parameter Model - Conceptual Development of the Rasch Model - The One-Parameter Model - The One-Parameter Logistic Model and the Rasch Model - Assumptions underlying the Model - An Empirical Data Set: The Mathematics Data Set - Conceptually Estimating an Individual's Location - Some Pragmatic Characteristics of Maximum Likelihood Estimates - The Standard Error of Estimate and Information - An Instrument's Estimation Capacity - Summary 3. Joint Maximum Likelihood Parameter Estimation - Joint Maximum Likelihood Estimation - Indeterminacy of Parameter Estimates - How Large a Calibration Sample? - Example: Application of the Rasch Model to the Mathematics Data, JMLE - Summary 4. Marginal Maximum Likelihood Parameter Estimation - Marginal Maximum Likelihood Estimation - Estimating an Individual's Location: Expected A Posteriori - Example: Application of the Rasch Model to the Mathematics Data, MMLE - Metric Transformation and the Total Characteristic Function - Summary 5. The Two-Parameter Model - Conceptual Development of the Two-Parameter Model - Information for the Two-Parameter Model - Conceptual Parameter Estimation for the 2PL Model - How Large a Calibration Sample? - Metric Transformation, 2PL Model - Example: Application of the 2PL Model to the Mathematics Data, MMLE - Information and Relative Efficiency - Summary 6. The Three-Parameter Model - Conceptual Development of the Three-Parameter Model - Additional Comments about the Pseudo-Guessing Parameter, X? - Conceptual Estimation for the 3PL Model - How Large a Calibration Sample? - Assessing Conditional Independence - Example: Application of the 3PL Model to the Mathematics Data, MMLE - Assessing Person Fit: Appropriateness Measurement - Information for the Three-Parameter Model - Metric Transformation, 3PL Model - Handling Missing Responses - Issues to Consider in Selecting among the 1PL, 2PL, and 3PL Models - Summary 7. Rasch Models for Ordered Polytomous Data - Conceptual Development of the Partial Credit Model - Conceptual Parameter Estimation of the PC Model - Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE - The Rating Scale Model - Conceptual Estimation of the RS Model - Example: Application of the RS Model to an Attitudes toward Condom Scale, JMLE - How Large a Calibration Sample? - Information for the PC and RS Models - Metric Transformation, PC and RS Models - Summary 8. Non-Rasch Models for Ordered Polytomous Data - The Generalized Partial Credit Model - Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE - Conceptual Development of the Graded Response Model - How Large a Calibration Sample? - Example: Application of the GR Model to an Attitudes toward Condom Scale, MMLE - Information for Graded Data - Metric Transformation, GPC and GR Models - Summary 9. Models for Nominal Polytomous Data - Conceptual Development of the Nominal Response Model - How Large a Calibration Sample? - Example: Application of the NR Model to a Science Test, MMLE - Example: Mixed Model Calibration of the Science Test--NR and PC Models, MMLE - Example: NR and PC Mixed Model Calibration of the Science Test, Collapsed Options, MMLE - Information for the NR Model - Metric Transformation, NR Model - Conceptual Development of the Multiple-Choice Model - Example: Application of the MC Model to a Science Test, MMLE - Example: Application of the BS Model to a Science Test, MMLE - Summary 10. Models for Multidimensional Data - Conceptual Development of a Multidimensional IRT Model - Multidimensional Item Location and Discrimination - Item Vectors and Vector Graphs - The Multidimensional Three-Parameter Logistic Model - Assumptions of the MIRT Model - Estimation of the M2PL Model - Information for the M2PL Model - Indeterminacy in MIRT - Metric Transformation, M2PL Model - Example: Application of the M2PL Model, Normal-Ogive Harmonic Analysis Robust Method - Obtaining Person Location Estimates - Summary 11. Linking and Equating - Equating Defined - Equating: Data Collection Phase - Equating: Transformation Phase - Example: Application of the Total Characteristic Function Equating - Summary 12. Differential Item Functioning - Differential Item Functioning and Item Bias - Mantel–Haenszel Chi-Square - The TSW Likelihood Ratio Test - Logistic Regression - Example: DIF Analysis - Summary Appendix A. Maximum Likelihood Estimation of Person Locations - Estimating an Individual's Location: Empirical Maximum Likelihood Estimation - Estimating an Individual's Location: Newton's Method for MLE - Revisiting Zero Variance Binary Response Patterns Appendix B. Maximum Likelihood Estimation of Item Locations Appendix C. The Normal Ogive Models - Conceptual Development of the Normal Ogive Model - The Relationship between IRT Statistics and Traditional Item Analysis Indices - Relationship of the Two-Parameter Normal Ogive and Logistic Models - Extending the Two-Parameter Normal Ogive Model to a Multidimensional Space Appendix D. Computerized Adaptive Testing - A Brief History - Fixed-Branching Techniques - Variable-Branching Techniques - Advantages of Variable-Branching over Fixed-Branching Methods - IRT-Based Variable-Branching Adaptive Testing Algorithm Appendix E. Miscellanea - Linear Logistic Test Model (LLTM) - Using Principal Axis for Estimating Item Discrimination - Infinite Item Discrimination Parameter Estimates - Example: NOHARM Unidimensional Calibration - An Approximate Chi-Square Statistic for NOHARM - Mixture Models - Relative Efficiency, Monotonicity, and Information - FORTRAN Formats - Example: Mixed Model Calibration of the Science Test--NR and 2PL Models, MMLE - Example: Mixed Model Calibration of the Science Test--NR and GR Models, MMLE - Odds, Odds Ratios, and Logits - The Person Response Function - Linking: A Temperature Analogy Example - Should DIF Analyses Be Based on Latent Classes? - The Separation and Reliability Indices - Dependency in Traditional Item Statistics and Observed Scores References Author Index Subject Index


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Product Details
  • ISBN-13: 9781593858698
  • Publisher: Guilford Publications
  • Publisher Imprint: Guilford Publications
  • Depth: 25
  • Height: 254 mm
  • No of Pages: 448
  • Series Title: Methodology in the Social Sciences
  • Weight: 951 gr
  • ISBN-10: 1593858698
  • Publisher Date: 01 Oct 2008
  • Binding: Hardback
  • Edition: 1
  • Language: English
  • Returnable: N
  • Spine Width: 25 mm
  • Width: 183 mm


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