English | 2019 | ISBN: 1138542785 | 280 Pages | PDF | 10 MB
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.