Using R for Item Response Theory Model Applications (Paperback)

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.

General

Imprint:	Routledge
Country of origin:	United Kingdom
Release date:	September 2019
First published:	2020
Authors:	Insu Paek • Ki Cole
Dimensions:	234 x 156 x 17mm (L x W x T)
Format:	Paperback
Pages:	272
ISBN-13:	978-1-138-54279-2
Categories:	Books > Reference & Interdisciplinary > Communication studies > Research methods Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Psychology > Philosophy & theory of psychology > General Books > Social sciences > Psychology > Psychological methodology > General
LSN:	1-138-54279-2
Barcode:	9781138542792

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