Using R for Item Response Theory Model Applications

✍ Scribed by Insu Paek (Author); Ki Cole (Author)

Publisher: Routledge
Year: 2019
Leaves: 281
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. Using R for Item Response Theory Model Applications is a practical guide for students, instructors, practitioners, and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data.

This book provides practical line-by-line descriptions of how to use R IRT packages for various IRT models. The scope and coverage of the modeling in the book covers almost all models used in practice and in popular research, including:

dichotomous response modeling

polytomous response modeling

mixed format data modeling

concurrent multiple group modeling

fixed item parameter calibration

modelling with latent regression to include person-level covariate(s)

simple structure, or between-item, multidimensional modeling

cross-loading, or within-item, multidimensional modeling

high-dimensional modeling

bifactor modeling

testlet modeling

two-tier modeling

For beginners, this book provides a straightforward guide to learn how to use R for IRT applications. For more intermediate learners of IRT or users of R, this book will serve as a great time-saving tool for learning how to create the proper syntax, fit the various models, evaluate the models, and interpret the output using popular R IRT packages.

✦ Table of Contents

Preface

1. Introduction

2. Unidimensional IRT with Dichotomous Item Responses

3. Unidimensional IRT with Polytomous Item Responses

4. Unidimensional IRT for Other Applications

5. Multidimensional IRT for Simple Structure

6. Multidimensional IRT for Bifactor Structure

7. Limitations and Caveat

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