Using R for Item Response Theory Model Applications

<p>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. <i>Using R for Item Response Theory Model Applications</i> 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.</p><p>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: </p><ul> <p> </p> <li>dichotomous response modeling</li> <p> </p> <li>polytomous response modeling</li> <p> </p> <li>mixed format data modeling</li> <p> </p> <li>concurrent multiple group modeling</li> <p> </p> <li>fixed item parameter calibration</li> <p> </p> <li>modelling with latent regression to include person-level covariate(s)</li> <p> </p> <li>simple structure or between-item multidimensional modeling</li> <p> </p> <li>cross-loading or within-item multidimensional modeling</li> <p> </p> <li>high-dimensional modeling</li> <p> </p> <li>bifactor modeling</li> <p> </p> <li>testlet modeling</li> <p> </p> <li>two-tier modeling</li> </ul><p>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.</p>