Introduction to item response theory models and applications

著者

• Carlson, James E.

書誌事項

Introduction to item response theory models and applications

James E. Carlson

（Multivariate applications book series）

Routledge, 2021

• : hbk

注記

Includes bibliographical references (p. [157]-162) and index

内容説明・目次

内容説明

1. The author is forefront of the application of IRT (classic and innovative methodologies). 2. Covers all IRT in broad brushstrokes in an accessible manner. 3. Includes an abundance of original and secondary sources to facilitate learning, including further reading, simulated datasets, and graphics.

目次

Introduction Background and Terminology Contents of the Following Chapters Models for Dichotomously-Scored Items Introduction Classical Test theory Models The Model Item Parameters and their Estimates Test Parameters and their Estimates Item Response Theory Models Introduction The Normal Ogive Three-Parameter Item Response Theory Model The Three-Parameter Logistic (3PL) Model Special Cases: The Two-Parameter and One-Parameter Logistic Models Relationships Between Probabilities of Alternative Responses Transformations of Scale Effects of Changes in Parameters The Test Characteristic Function The Item Information Function The Test Information Function and Standard Errors of Measurement IRT Estimation Methodology Estimation of Item Parameters Estimation of Proficiency Indeterminacy of the Scale in IRT Estimation Summary Analyses of Dichotomously-Scored Item and Test Data Introduction Example Classical Test Theory Analyses with a Small Dataset Test and Item Analyses with a Larger Dataset CTT Item and Test Analysis Results IRT Item and Test Analysis IRT Software Missing Data Iterative Estimation Methodology Model Fit IRT Analyses Using PARSCALE PARSCALE Terminology Some PARSCALE Options PARSCALE Item Analysis PARSCALE Test Analyses IRT Analyses Using flexMIRT flexMIRT Terminology Some flexMIRT Options flexMIRT Item Analyses and Comparisons Between Programs flexMIRT Test Analyses and Comparisons Between Programs Using IRT Results to Evaluate Items and Tests Evaluating Estimates of Item Parameters Evaluating Fit of Models to Items Evaluating Tests as a Whole or Subsets of Test Items Equating, Linking, and Scaling Equating Linking Scaling Vertical Scaling Summary Models for Polytomously-Scored Items Introduction The Nature of Polytomously-Scored Items Conditional Probability Forms of Models for Polytomous Items Probability-of-Response Form of the Polytomous Models The 2PPC Model The GPC Model The Graded Response (GR) Model Additional Characteristics of the GPC Model Effects of Changes in Parameters Alternative Parameterizations The Expected Score Function Functions of Scoring at or Above Categories Comparison of Conditional Response and P+ Functions Item Mapping and Standard Setting The Test Characteristic Function The Item Information Function The Item Category Information Function The Test Information Function Conditional Standard Errors of Measurement Summary Analyses of Polytomously-Scored Item and Test Data Generation of Example Data Classical Test Theory Analyses Item Analyses Test Analyses IRT Analyses PARSCALE Item Analyses flexMIRT Item Analyses and Comparisons with PARSCALE Additional Methods of Using IRT Results to Evaluate Items Evaluating Estimates of Item Parameters Evaluating Fit of Models to Item Data Additional Graphical Methods Test Analyses PARSCALE Test Analyses flexMIRT Test Analyses Placing the Results from Different Analyses on the Same Scale Summary Multidimensional Item Response Theory Models Introduction The Multidimensional 3PL Model for Dichotomous Items The Multidimensional 2PL Model for Dichotomous Items Is there a Multidimensional 1PL Model for Dichotomous Items Further Comments on MIRT Models Alternate Parameterizations Additional Analyses of MIRT Data Noncompensatory MIRT Models MIRT Models for Polytomous Data Summary Analyses of Multidimensional Item Response Data Response Data Generation MIRT Computer Software MIRT and Factor analyses flexMIRT analyses of Example Generated Data One-dimensional Solution with Two-Dimensional Data Two-dimensional Solution Summary Overview of More Complex Item Response Theory Models Some More Complex Unidimensional Models Multigroup Models Adaptive Testing Mixture Models Hierarchical Rater Models Testlet Models More General MIRT Models: Some Further Reading Hierarchical Models Cognitive Diagnostic Models Summary References Appendix A. Some Technical Background 1. Slope of the 3PL Curve at the Inflection Point where 2. Simplifying Notation for GPC Expressions 3. Some Characteristics of GPC Model Items Peaks of Response Curves Crossing Point of Pk and Pk-1 Crossing Point of P0 and P2 for m = 3 Symmetry in the Case of m = 3 Limits of the Expected Score Function Appendix B. Item Category Information Functions Appendix C. Item Generating Parameters and Classical and IRT Parameter Estimates Index

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詳細情報

• NII書誌ID(NCID): BC01808228
• ISBN:
  • 9780367476922
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: New York
• ページ数/冊数: xiii, 165 p.
• 大きさ: 27 cm
• 分類:
  • DC23 : 150.287
• 件名:
  • LCSH : Item response theory
  • LCSH : Psychometrics
  • LCSH : Psychology -- Mathematical models
• 親書誌ID:
  • BA36860150