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.
“A fascinating story of a young mixed-race man caught between two cultures, not knowing what to keep and what to leave behind.”—James McBride, author of The Heaven & Earth Grocery Store A “magnificent” (Ha Jin), “mesmerizing” (James McBride), and “magical” (Marie Myung-Ok Lee) fever dream of a novel that interweaves the coming-of-age of a 1970s Korean-American boy grappling with his identity and the impact of intergenerational trauma. Growing up outside a US military base in South Korea in the aftermath of the Vietnam War, Insu—the son of a Korean mother and a German father enlisted in the US Army—spends his days with his “half and half” friends skipping school, selling scavenged Western goods on the black market, watching Hollywood movies, and testing the boundaries between childhood and adulthood. When he hears a legend that water collected in a human skull will cure any sickness, he vows to dig up a skull in order to heal his ailing Big Uncle, a geomancer who has been exiled by the family to a mountain cave to die. Insu’s quest takes him and his friends on a sprawling, wild journey into some of South Korea’s darkest corners, opening them up to a fantastical world beyond their grasp. Meanwhile, Big Uncle has embraced his solitude and fate, trusting in otherworldly forces Insu cannot access. As he recalls his wartime experiences of betrayal and lost love, Big Uncle attempts to teach his nephew that life is not limited to what we can see—or think we know. Largely autobiographical and sparkling with magical realism, Skull Water is the story of a boy coming into his own—and the ways the past haunts the present, in a country on the cusp of modernity, struggling to confront its troubled history. As Insu seeks the wisdom of his ancestors, what he learns, he hopes, will save not just his uncle but himself.
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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