Polytomous Item Response Theory Models
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Polytomous Item Response Theory Models
This volume provides an introduction to the range of polytomous models available within item response theory. It begins by outlining the distinction between the two major types of polytomous IRT models, then goes on to describe them in detail.
Introduction to Nonparametric Item Response Theory
This book introduces social and behavioral science students and researchers to the theory and practice of the highly powerful methods of nonparametric item response theory (IRT). Anyone who uses or constructs tests or questionnaires for measuring abilities, achievements, personality traits, attitudes, or opinions will find nonparametric IRT useful for designing and improving such measurements. The authors show how the broadness of the nonparametric item response models allows them to fit many data sets and remain powerful enough for implying useful measurement properties, such as the ordering of persons using the simple total score (number-correct for dichotomous item tests and sum of rating scale score for polytomous item tests) and the ordering of the items using the item means.
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.