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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.
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.
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