Introduction to Item Response Theory Models and Applications de James Carlson

Introduction to Item Response Theory Models and Applications: Multivariate Applications Series

Autor James Carlson

en Limba Engleză Paperback – 13 oct 2020

This is a highly accessible, comprehensive introduction to item response theory (IRT) models and their use in various aspects of assessment/testing. The book employs a mixture of graphics and simulated data sets to ease the reader into the material and covers the basics required to obtain a solid grounding in IRT.
Written in an easily accessible way that assumes little mathematical knowledge, Carlson presents detailed descriptions of several commonly used IRT models, including those for items scored on a two-point (dichotomous) scale such as correct/incorrect, and those scored on multiple-point (polytomous) scales, such as degrees of correctness. One chapter describes a model in-depth and is followed by a chapter of instructions and illustrations showing how to apply the models to the reader’s own work.
This book is an essential text for instructors and higher level undergraduate and postgraduate students of statistics, psychometrics, and measurement theory across the behavioral and social sciences, as well as testing professionals.

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Specificații

ISBN-13: 9780367471019
ISBN-10: 0367471019
Pagini: 182
Ilustrații: 107 Line drawings, black and white; 17 Tables, black and white; 124 Illustrations, black and white
Dimensiuni: 178 x 254 x 10 mm
Greutate: 0.33 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Seria Multivariate Applications Series

Locul publicării:Oxford, United Kingdom

Public țintă

Postgraduate and Undergraduate Advanced

Cuprins

Introduction
1. Background and Terminology
2. Contents of the Following Chapters
Models for Dichotomously-Scored Items
1. Introduction
2. Classical Test theory ModelsThe Model
  Item Parameters and their Estimates
  Test Parameters and their Estimates
3. Item Response Theory ModelsIntroduction
  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
4. IRT Estimation MethodologyEstimation of Item Parameters
  Estimation of Proficiency
  Indeterminacy of the Scale in IRT Estimation
5. Summary
Analyses of Dichotomously-Scored Item and Test Data
1. Introduction
2. Example Classical Test Theory Analyses with a Small Dataset
3. Test and Item Analyses with a Larger DatasetCTT Item and Test Analysis Results
4. IRT Item and Test AnalysisIRT Software
  Missing Data
  Iterative Estimation Methodology
  Model Fit
5. IRT Analyses Using PARSCALEPARSCALE Terminology
  Some PARSCALE Options
  PARSCALE Item Analysis
  PARSCALE Test Analyses
6. IRT Analyses Using flexMIRTflexMIRT Terminology
  Some flexMIRT Options
  flexMIRT Item Analyses and Comparisons Between Programs
  flexMIRT Test Analyses and Comparisons Between Programs
7. Using IRT Results to Evaluate Items and TestsEvaluating Estimates of Item Parameters
  Evaluating Fit of Models to Items
  Evaluating Tests as a Whole or Subsets of Test Items
8. Equating, Linking, and ScalingEquating
  Linking
  Scaling
  Vertical Scaling
9. Summary
Models for Polytomously-Scored Items
1. Introduction
2. The Nature of Polytomously-Scored Items
3. Conditional Probability Forms of Models for Polytomous Items
4. Probability-of-Response Form of the Polytomous ModelsThe 2PPC Model
  The GPC Model
  The Graded Response (GR) Model
5. Additional Characteristics of the GPC ModelEffects 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
6. Summary
Analyses of Polytomously-Scored Item and Test Data
1. Generation of Example Data
2. Classical Test Theory AnalysesItem Analyses
  Test Analyses
3. IRT AnalysesPARSCALE Item Analyses
  flexMIRT Item Analyses and Comparisons with PARSCALE
4. Additional Methods of Using IRT Results to Evaluate ItemsEvaluating Estimates of Item Parameters
  Evaluating Fit of Models to Item Data
  Additional Graphical Methods
5. Test AnalysesPARSCALE Test Analyses
  flexMIRT Test Analyses
6. Placing the Results from Different Analyses on the Same Scale
7. Summary
Multidimensional Item Response Theory Models
1. Introduction
2. The Multidimensional 3PL Model for Dichotomous Items
3. The Multidimensional 2PL Model for Dichotomous Items
4. Is there a Multidimensional 1PL Model for Dichotomous Items
5. Further Comments on MIRT ModelsAlternate Parameterizations
  Additional Analyses of MIRT Data
6. Noncompensatory MIRT Models
7. MIRT Models for Polytomous Data
8. Summary
Analyses of Multidimensional Item Response Data
1. Response Data Generation
2. MIRT Computer Software
3. MIRT and Factor analyses
4. flexMIRT analyses of Example Generated DataOne-dimensional Solution with Two-Dimensional Data
  Two-dimensional Solution
5. Summary
Overview of More Complex Item Response Theory Models
1. Some More Complex Unidimensional ModelsMultigroup Models
  Adaptive Testing
  Mixture Models
  Hierarchical Rater Models
  Testlet Models
2. More General MIRT Models: Some Further ReadingHierarchical Models
3. Cognitive Diagnostic Models
4. 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 P_k and P_k-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

Notă biografică

James E. Carlson received his Ph.D. from the University of Alberta, Canada, specializing in applied statistics. He was professor of education at the universities of Pittsburgh, USA, and Ottawa, Canada. He also held psychometric positions at testing organizations and the National Assessment Governing Board, U. S. Department of Education. He is a former editor of the Journal of Educational Measurement and has authored two book chapters and a number of journal articles and research reports.

Recenzii

"Carlson’s book is a very clear and well-written introduction to item response theory models that should prove very useful to a wide range of students, instructors, researchers and professionals who want to understand the basics of this useful methodology." -- Lisa L. Harlow, professor of psychology at the University of Rhode Island, USA, and series editor for the Multivariate Applications Series (sponsored by SMEP).

Descriere

This is a highly accessible, comprehensive introduction to item response theory (IRT) models and their use in various aspects of assessment/testing. The book employs a mixture of graphics and simulated data sets to ease the reader into the material and covers the basics required to obtain a solid grounding in IRT.