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Introduction to Item Response Theory Models and Applications book cover

1st Edition

Introduction to Item Response Theory Models and Applications

By James Carlson Copyright 2021
    182 Pages 124 B/W Illustrations
    by Routledge

    182 Pages 124 B/W Illustrations
    by Routledge

    182 Pages 124 B/W Illustrations
    by Routledge
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    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.

    1. Introduction

      1. Background and Terminology

      2. Contents of the Following Chapters

    2. Models for Dichotomously-Scored Items

      1. Introduction

      2. Classical Test theory Models

        3. The Model

        Item Parameters and their Estimates

        Test Parameters and their Estimates

      3. Item Response Theory Models

        4. 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

      4. IRT Estimation Methodology

        5. Estimation of Item Parameters

        Estimation of Proficiency

        Indeterminacy of the Scale in IRT Estimation

      5. Summary

    3. 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 Dataset

        4. CTT Item and Test Analysis Results

      4. IRT Item and Test Analysis

        5. IRT Software

        Missing Data

        Iterative Estimation Methodology

        Model Fit

      5. IRT Analyses Using PARSCALE

        6. PARSCALE Terminology

        Some PARSCALE Options

        PARSCALE Item Analysis

        PARSCALE Test Analyses

      6. IRT Analyses Using flexMIRT

        7. flexMIRT 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 Tests

        8. Evaluating Estimates of Item Parameters

        Evaluating Fit of Models to Items

        Evaluating Tests as a Whole or Subsets of Test Items

      8. Equating, Linking, and Scaling

        9. Equating

        Linking

        Scaling

        Vertical Scaling

      9. Summary

    4. 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 Models

        5. The 2PPC Model

        The GPC Model

        The Graded Response (GR) Model

      5. Additional Characteristics of the GPC Model

        6. 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

      6. Summary

    5. Analyses of Polytomously-Scored Item and Test Data

      1. Generation of Example Data

      2. Classical Test Theory Analyses

        3. Item Analyses

        Test Analyses

      3. IRT Analyses

        4. PARSCALE Item Analyses

        flexMIRT Item Analyses and Comparisons with PARSCALE

      4. Additional Methods of Using IRT Results to Evaluate Items

        5. Evaluating Estimates of Item Parameters

        Evaluating Fit of Models to Item Data

        Additional Graphical Methods

      5. Test Analyses

        6. PARSCALE Test Analyses

        flexMIRT Test Analyses

      6. Placing the Results from Different Analyses on the Same Scale

      7. Summary

    6. 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 Models

        6. Alternate Parameterizations

        Additional Analyses of MIRT Data

      6. Noncompensatory MIRT Models

      7. MIRT Models for Polytomous Data

      8. Summary

    7. 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 Data

        5. One-dimensional Solution with Two-Dimensional Data

        Two-dimensional Solution

      5. Summary

    8. Overview of More Complex Item Response Theory Models

      1. Some More Complex Unidimensional Models

        2. Multigroup Models

        Adaptive Testing

        Mixture Models

        Hierarchical Rater Models

        Testlet Models

      2. More General MIRT Models: Some Further Reading

        3. Hierarchical 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 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

    Biography

    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.

    "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).

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