Insu Paek, Ki Cole

Using R for Item Response Theory Model Applications (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 280
• Erscheinungstermin: 16. September 2019
• Englisch
• ISBN-13: 9781351008150
• Artikelnr.: 57730514

Autorenporträt

Insu Paek is an associate professor at Florida State University. Before he came to Florida State University, he worked as a psychometrician for large-scale assessment programs and in testing companies for several years. His research interests are educational and psychological measurement and item response modeling and its application.

Ki Cole is an assistant professor at Oklahoma State University. She teaches graduate level educational statistics courses, including item response theory and factor analysis for the behavioural sciences. Her research interests include the theory and applications of psychometrics, scale development, understanding response tendencies, and software evaluation.

Inhaltsangabe

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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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

Rezensionen

This book is a great introduction to both the R program for item response theory (IRT) analysis and IRT modeling itself for readers who want a firm grasp on both skills. This book is a helpful manual for understanding IRT concepts and background concisely and for easily learning the freely accessible R programs for doing IRT analyses. Reading this book is like catching two birds with one stone. This book is clearly written and amazingly easy to follow from installing the R program and executing the R command to interpreting the executed results. This book also provides practical advice, guidance, and specific details on utilizing the R program for practitioners, researchers, and graduate students in an introductory or an intermediate IRT course. The authors explain the essential background of various IRT models and the logic of each model in terms of practical applications. A major strength of the book is its consistent use of detailed examples to illustrate the concepts, the practical applications of IRT, and the thorough explanation of results for the executed examples. Another strong point is the general approachability and clarity of the text. Both practical users of IRT and beginners of the R program will benefit from this clear text and detailed examples. As a practitioner who frequently uses IRT but has little experience with R for IRT analysis, I strongly recommend this book as a learning tool for both IRT and R IRT programs.

Dr. Hyeonjoo Oh, Senior psychometrician, Educational Testing Service