Multidimensional Item Response Theory

Multidimensional Item Response Theory
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Описание книги

Several decades of psychometric research have led to the development of sophisticated models for multidimensional test data, and in recent years, multidimensional item response theory (MIRT) has become a burgeoning topic in psychological and educational measurement. Considered a cutting-edge statistical technique, the methodology underlying MIRT can be complex, and therefore doesn’t receive much attention in introductory IRT courses. However author Wes Bonifay shows how MIRT can be understood and applied by anyone with a firm grounding in unidimensional IRT modeling. His volume includes practical examples and illustrations, along with numerous figures and diagrams. Multidimensional Item Response Theory includes snippets of R code interspersed throughout the text (with the complete R code included on an accompanying website) to guide readers in exploring MIRT models, estimating the model parameters, generating plots, and implementing the various procedures and applications discussed throughout the book.

Оглавление

Wes Bonifay. Multidimensional Item Response Theory

Quantitative Applications in the Social Sciences

Multidimensional Item Response Theory

Contents

Series Editor’s Introduction

Acknowledgments

About the Author

Chapter 1 Introduction

Chapter 2 Unidimensional Item Response Theory

What Is a Latent Trait?

Assumptions of UIRT

UIRT Models for Dichotomous Data

R Code

UIRT Models for Polytomous Data

Difference Models

Divide-by-Total Models

Additional UIRT Models

R Code

UIRT Estimation

Joint Estimation of Both Person and Item Parameters

The EM Algorithm

Scoring

Other Estimation Methods

R Code

Descriptions of Images and Figures

Chapter 3 MIRT Models for Dichotomous Data

Compensation in MIRT Modeling

Compensatory MIRT Models

R Code

Partially Compensatory MIRT Models

R Code

Additional MIRT Models for Dichotomous Data

R Code

Recent Advances in Dichotomous MIRT Modeling

Descriptions of Images and Figures

Chapter 4 MIRT Models for Polytomous Data

R Code

Additional Polytomous MIRT Models

Descriptions of Images and Figures

Chapter 5 Descriptive MIRT Statistics

The θ-Space

The Item Response Surface

Conditional Response Functions

The Direction of Measurement

Person Parameters in MIRT

MIRT Information

Polytomous MIRT Descriptives

Test-Level MIRT Descriptives

Descriptions of Images and Figures

Chapter 6 Item Factor Structures

Two-Tier Model

Correlated-Traits Model

Bifactor Model

Testlet Response Model

R Code

Descriptions of Images and Figures

Chapter 7 Estimation in MIRT Models

Conceptual Illustration

Missing Data Formulation

Two Challenges

Adaptive Quadrature

Bayesian Estimation

MH-RM Estimation

R Code

Descriptions of Images and Figures

Chapter 8 MIRT Model Diagnostics and Evaluation

Dimensionality Assessment

Test-Level Fit Assessment

Item-Level Fit Assessment

Model Comparison Methods

Chapter 9 MIRT Applications

Linking and Equating

R Code

Differential Item Functioning

R Code

Computerized Adaptive Testing

R Code

Applications of the Two-Tier Item Factor Structure

Further MIRT Applications

Descriptions of Images and Figures

References

Index

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A Sage Publications Series

Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne

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Series Editor

Chapter 5 covers several ways of describing the results of a MIRT analysis. One of the many challenges in understanding MIRT models is how to make sense of the parameter estimates and other statistical properties of multidimensional items/tests. This chapter presents both item- and test-level descriptives, including multidimensional item response surfaces, information functions, and other important components of standard MIRT output.

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