Using R for Item Response Theory Model Applications

1. Auflage 2019
ISBN: 978-1-138-54278-5
Verlag: Routledge

Buch, Englisch, 280 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 590 g

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.

Zielgruppe

Postgraduate and Professional

Autoren/Hrsg.

Paek, Insu

Cole, Ki

Fachgebiete

• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

Inhaltsverzeichnis

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

Über Autor(innen)

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.