Using R for Item Response Theory Model Applications

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. Content: Preface -- 1. Introduction -- 2. Unidimensional IRT with dichotomous item responses -- 3. Unidimensional IRT with polytomous item responses -- 4. Unidimensional IRT with other applications -- 5. Multidimensional IRT for simple structure -- 6. Multidimensional IRT for bifactor structure -- 7. Limitations and caveat -- Index. This book introduces basic concepts of IRT and detailed information on how to use R IRT programs for IRT applications.