Name: Using r for Item Response Theory Model Applications (en Inglés)
SKU: 52074615
Price: 85.27 USD
Availability: InStock
Author: Insu Paek
ISBN: 9781138542792

portada Using r for Item Response Theory Model Applications (en Inglés)

Using r for Item Response Theory Model Applications (en Inglés)

Formato

Libro Físico

Editorial

Routledge

Autor

Insu Paek

Año

2019

Idioma

Inglés

N° páginas

280

Encuadernación

Tapa Blanda

ISBN13

9781138542792

N° edición

Categorías

Metodología psicológica

Using r for Item Response Theory Model Applications (en Inglés)

Insu Paek (Autor) · Routledge · Tapa Blanda

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Reseña del libro "Using r for Item Response Theory Model Applications (en Inglés)"

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.