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:
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- dichotomous response modeling
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- polytomous response modeling
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- mixed format data modeling
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- concurrent multiple group modeling
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- fixed item parameter calibration
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- modelling with latent regression to include person-level covariate(s)
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- simple structure, or between-item, multidimensional modeling
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- cross-loading, or within-item, multidimensional modeling
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- high-dimensional modeling
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- bifactor modeling
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- testlet modeling
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- 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.