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:
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.
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
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