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This book has been replaced by The Theory and Practice of Item Response Theory, Second Edition, ISBN 978-1-4625-4775-3.

Tabla de contenidos provista por Syndetics

• Symbols and Acronyms(p. xiii)
• 1 Introduction to Measurement(p. 1)
• Measurement(p. 1)
• Some Measurement Issues(p. 3)
• Item Response Theory(p. 4)
• Classical Test Theory(p. 5)
• Latent Class Analysis(p. 7)
• Summary(p. 9)
• 2 The One-Parameter Model(p. 11)
• Conceptual Development of the Rasch Model(p. 11)
• The One-Parameter Model(p. 16)
• The One-Parameter Logistic Model and the Rasch Model(p. 19)
• Assumptions Underlying the Model(p. 20)
• An Empirical Data Set: The mathematics Data Set(p. 21)
• Conceptually Estimating an Individual's Location(p. 22)
• Some Pragmatic Characteristics of Maximum Likelihood Estimates(p. 26)
• The Standard Error of Estimate and Information(p. 27)
• An Instrument's Estimation Capacity(p. 31)
• Summary(p. 34)
• 3 Joint Maximum Likelihood Parameter Estimation(p. 39)
• Joint Maximum Likelihood Estimation(p. 39)
• Indeterminacy of Parameter Estimates(p. 41)
• How Large a Calibration Sample?(p. 42)
• Example: Application of the Rasch Model to the Mathematics Data, JMLE(p. 43)
• Summary(p. 64)
• 4 Marginal Maximum Likelihood Parameter Estimation(p. 68)
• Marginal Maximum Likelihood Estimation(p. 68)
• Estimating an Individual's Location: Expected A Posteriori(p. 75)
• Example: Application of the Rasch Model to the Mathematics Data, MMLE(p. 80)
• Metric Transformation and the Total Characteristic Function(p. 92)
• Summary(p. 96)
• 5 The Two-Parameter Model(p. 99)
• Conceptual Development of the Two-Parameter Model(p. 99)
• Information for the Two-Parameter Model(p. 101)
• Conceptual Parameter estimation for the 2PL Model(p. 103)
• How Large a Calibration Sample?(p. 104)
• Metric Transformation, 2PL Model(p. 106)
• Example: Application of the 2PL Model to the Mathematics Data, MMLE(p. 107)
• Fit Assessment: An Alternative Approach for Assessing Invariance(p. 110)
• Information and Relative Efficiency(p. 114)
• Summary(p. 118)
• 6 The Three-Parameter Model(p. 123)
• Conceptual Development of the Three-Parameter Model(p. 123)
• Additional Comments about the Pseudo-Guessing Parameter, Xj(p. 126)
• Conceptual Parameter Estimation for the 3PL Model(p. 127)
• How Large a Calibration Sample?(p. 130)
• Assessing Conditional Independence(p. 131)
• Example: Application of the 3PL Model to the Mathematics Data, MMLE(p. 134)
• Assessing Person Fit: Appropriateness Measurement(p. 142)
• Information for the Three-Parameter Model(p. 144)
• Metric Transformation, 3PL Model(p. 147)
• Handling Missing Responses(p. 148)
• Issues to Consider in selecting among the 1PL, 2PL, and 3PL Models(p. 152)
• Summary(p. 154)
• 7 Rasch Models for Ordered Polytomous Data(p. 162)
• Conceptual Development of the Partial Credit Model(p. 163)
• Conceptual Parameter Estimation of the PC Model(p. 169)
• Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE(p. 169)
• The Rating Scale Model(p. 179)
• Conceptual Estimation of the RS Model(p. 184)
• Example: Application of the RS Model to an Attitudes Towards Condoms Scale, JMLE(p. 184)
• How Large a Calibration Sample?(p. 198)
• Information for the PC and RS Models(p. 200)
• Metric Transformation, PC and RS Models(p. 201)
• Summary(p. 202)
• 8 Non-Rasch Models for Ordered Polytomous Data(p. 209)
• The Generalized Partial Credit Model(p. 209)
• Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE(p. 214)
• Conceptual Development of the Graded Response Model(p. 217)
• How Large a Calibration Sample?(p. 223)
• Example: Application of the GR Model to an Attitudes Towards Condoms Scale, MMLE(p. 224)
• Information for Graded Data(p. 230)
• Metric Transformation, GPC and GR Models(p. 233)
• Summary(p. 234)
• 9 Model for Nominal Polytomous Data(p. 237)
• Conceptual Development of the Nominal Response Model(p. 238)
• How Large a Calibration Sample?(p. 246)
• Example: Application of the NR Model to a Science Test, MMLE(p. 248)
• Example: Mixed Model Calibration of the Science Test-NR and PC Models, MMLE(p. 251)
• Example: NR and PC Mixed Model Calibration of the Science Test, Collapsed Options, MMLE(p. 254)
• Information for the NR Model(p. 259)
• Metric Transformation, NR Model(p. 261)
• Conceptual Development of the Multiple-Choice Model(p. 261)
• Example: Application of the MC Model to a Science Test, MMLE(p. 263)
• Example: Application of the BS Model to a Science Test, MMLE(p. 269)
• Summary(p. 272)
• 10 Models for Multidimensional Data(p. 275)
• Conceptual Development of a Multidimensional IRT Model(p. 275)
• Multidimensional Item Location and Discrimination(p. 281)
• Item Vectors and Vector Graphs(p. 285)
• The Multidimensional Three-Parameter Logistic Model(p. 288)
• Assumptions of the MIRT Model(p. 288)
• Estimation of the M2PL Model(p. 289)
• Information for the M2PL Model(p. 290)
• Indeterminacy in MIRT(p. 291)
• Metric Transformation, M2PL Model(p. 294)
• Example: Application of the M2PL Model, Normal-Ogive Harmonic Analysis Robust Method(p. 296)
• Obtaining Person Location Estimates(p. 302)
• Summary(p. 303)
• 11 Linking and Equating(p. 306)
• Equating Defined(p. 306)
• Equating: Data Collection Phase(p. 307)
• Equating: Transformation Phase(p. 309)
• Example: Application of the Total Characteristic Function Equating Method(p. 316)
• Summary(p. 318)
• 12 Differential Item Functioning(p. 323)
• Differential Item Functioning and Item Bias(p. 324)
• Mantel-Haenszel Chi-Sqyare(p. 327)
• The TSW Likelihood Ratio Test(p. 330)
• Logistic Regression(p. 331)
• Example: DIF Analysis(p. 334)
• Summary(p. 343)
• Appendix A Maximum Likelihood Estimation of Person Locations(p. 347)
• Estimating and Individual's Location: Empirical Maximum Likelihood Estimation(p. 347)
• Estimating and Individual's Location: Newton's Method for MLE(p. 348)
• Revisiting Zero Variance Binary Response Patterns(p. 354)
• Appendix B Maximum Likelihood Estimation of Item Locations(p. 356)
• Appendix C The Normal Ogive Models(p. 360)
• Conceptual Development of the Normal Ogive Model(p. 360)
• The Relationship between IRT Statistics and Traditional Item Analysis Indices(p. 365)
• Relationship of the Two-Parameter Normal Ogive and Logistic Model(p. 368)
• Extending the Two-Parameter Normal Ogive Model to a Multidimensional Space(p. 370)
• Appendix D Computerized Adaptive Testing(p. 373)
• A Brief History(p. 373)
• Fixed-Branching Techniques(p. 374)
• Variable-Branching Techniques(p. 375)
• Advantages of Variable-Branching over Fixed-Branching Methods(p. 375)
• IRT-Based Variable-Branching Adaptive Testing Algorithm(p. 376)
• Appendix E Miscellanea(p. 382)
• Linear Logistic Test Model (LLTM)(p. 382)
• Using Principal Axis for Estimating Item Discrimination(p. 384)
• Infinite Item Discrimination parameter Estimates(p. 385)
• Example: NOHARM Unidimensional Calibration(p. 387)
• An Approximate Chi-Square Statistic for NOHARM(p. 389)
• Mixture Models(p. 391)
• Relative Efficiency, Monotonicity, and Information(p. 393)
• FORTRAN Formats(p. 395)
• Example: Mixed Model Calibration of the Science Test-NR and 2PL Models, MMLE(p. 396)
• Example: Mixed Model Calibration of the Science Test-NR and GR Models, MMLE(p. 399)
• Odds, Odds Ratios, and Logits(p. 399)
• The Person Response Function(p. 403)
• Linking: A Temperature Analogy Example(p. 405)
• Should DIF Analyses Be Based on Latent Classes?(p. 407)
• The Separation and Reliability Indices(p. 408)
• Dependency in Traditional Item Statistics and Observed Scores(p. 409)
• References(p. 419)
• Author Index(p. 439)
• Subject Index(p. 444)
• About the Author(p. 448)

Notas de autor provistas por Syndetics

R. J. de Ayala is Professor of Educational Psychology at the University of Nebraska-Lincoln.