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A short guide to item response theory models

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A short guide to item response theory modelsby Gerhard Tutz.
作者:	Tutz, Gerhard.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xi, 255 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Item response theory.
電子資源:	https://doi.org/10.1007/978-3-031-87271-6
ISBN:	9783031872716$q(electronic bk.)

A short guide to item response theory models
Tutz, Gerhard.

A short guide to item response theory models[electronic resource] /by Gerhard Tutz. - Cham :Springer Nature Switzerland :2025. - xi, 255 p. :ill. (some col.), digital ;24 cm. - Statistics for social and behavioral sciences,2199-7365. - Statistics for social and behavioral sciences..

Preface -- Introduction -- The Binary Rasch Model -- Extensions of the Rasch Model and Alternative Binary Models -- Ordinal Models -- Extended Ordinal Models Accounting for Response Styles -- The Thresholds Model a Common Framework for Discrete and Continuous Responses -- Classical Test Theory -- Response Models for Count Data -- Tree-Based Item Response Models -- Differential Item Functioning -- Explanatory Item Response Models -- R Packages -- Examples -- Bibliography.

This book presents foundational concepts, essential principles, and practical applications of Item Response Theory (IRT). It provides a structured survey of diverse models that have been put forth, emphasizing both their differences and commonalities. The main focus is on modern latent trait theory models which provide measurement tools that clearly separate between person abilities and item parameters. The topics covered include the binary Rasch model, its extensions and alternative binary models, ordinal models and their extensions that account for response styles, the thresholds model, classical test theory, response models for count data, differential item functioning, and explanatory item response models. Tree-based item response models, typically not found in classical IRT textbooks, are also addressed. Applications of the models are illustrated on several data sets from differing areas, showing how models can be fitted and compared. All examples have been computed using R. Code snippets are provided, and the full R code for most of the examples is available online. The book is aimed at graduate students, applied statisticians, and researchers working in psychometrics, educators, and anyone curious about modeling strategies that enhance the precision and validity of their measurement tools. It serves as an introductory guide for beginners while also providing a resource for those seeking an overview of the plethora of available IRT models.

ISBN: 9783031872716$q(electronic bk.)

Standard No.: 10.1007/978-3-031-87271-6doiSubjects--Topical Terms:

186197
Item response theory.

LC Class. No.: BF39.2.I84

Dewey Class. No.: 150.15195

A short guide to item response theory models
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520 $a This book presents foundational concepts, essential principles, and practical applications of Item Response Theory (IRT). It provides a structured survey of diverse models that have been put forth, emphasizing both their differences and commonalities. The main focus is on modern latent trait theory models which provide measurement tools that clearly separate between person abilities and item parameters. The topics covered include the binary Rasch model, its extensions and alternative binary models, ordinal models and their extensions that account for response styles, the thresholds model, classical test theory, response models for count data, differential item functioning, and explanatory item response models. Tree-based item response models, typically not found in classical IRT textbooks, are also addressed. Applications of the models are illustrated on several data sets from differing areas, showing how models can be fitted and compared. All examples have been computed using R. Code snippets are provided, and the full R code for most of the examples is available online. The book is aimed at graduate students, applied statisticians, and researchers working in psychometrics, educators, and anyone curious about modeling strategies that enhance the precision and validity of their measurement tools. It serves as an introductory guide for beginners while also providing a resource for those seeking an overview of the plethora of available IRT models.
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