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.

chapter 1|13 pages


chapter 4|34 pages

Unidimensional Irt with Other Applications

chapter 5|41 pages

Multidimensional Irt for Simple Structure

chapter 7|3 pages

Limitations and Caveat