An Introduction to the Rasch Model with Examples in R

Matthew D. Zeigenfuse

ISBN 10: 1138710466 / ISBN 13: 9781138710467

Published by Chapman And Hall/CRC, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - An Introduction to the Rasch Model with Examples in R offers a clear, comprehensive introduction to the Rasch model along with practical examples in the free, open-source software R.It is accessible for readers without a background in psychometrics or statistics, while also providing detailed explanations of the relevant mathematical and statistical concepts for readers who want to gain a deeper understanding. Its worked examples in R demonstrate how to apply the methods to real-world examples and how to interpret the resulting output.In addition to motivating and presenting the Rasch model, the book covers different methods for parameter estimation and for assessing fit and differential item functioning (DIF). While focusing on the Rasch model, it also addresses a variety of other dichotomous and polytomous Rasch and item response theory (IRT) models, such as two-parameter logistic (2PL) and Partial Credit models, and extensions, including mixture Rasch models and computerized adaptive testing (CAT).Theory is presented in a self-contained way. All necessary mathematical and statistical background is contained in the chapters and appendices. The book also provides detailed, step-by-step instructions for getting started with R and using the eRm, mirt, TAM and rstan packages for fitting Rasch models.

Seller Inventory # 9781138710467

Bibliographic Details

• Title: An Introduction to the Rasch Model with Examples in R
• Publisher: Chapman And Hall/CRC
• Publication Date: 2022
• Language: English
• ISBN 10: 1138710466
• ISBN 13: 9781138710467
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 18D millimeters 234 millimeters width by 156 millimeters height by 18 millimeters depth
• Weight: 494 grams

About this title

Synopsis

An Introduction to the Rasch Model with Examples in R offers a clear, comprehensive introduction to the Rasch model along with practical examples in the free, open-source software R.

It is accessible for readers without a background in psychometrics or statistics, while also providing detailed explanations of the relevant mathematical and statistical concepts for readers who want to gain a deeper understanding. Its worked examples in R demonstrate how to apply the methods to real-world examples and how to interpret the resulting output.

In addition to motivating and presenting the Rasch model, the book covers different methods for parameter estimation and for assessing fit and differential item functioning (DIF). While focusing on the Rasch model, it also addresses a variety of other dichotomous and polytomous Rasch and item response theory (IRT) models, such as two-parameter logistic (2PL) and Partial Credit models, and extensions, including mixture Rasch models and computerized adaptive testing (CAT).

Theory is presented in a self-contained way. All necessary mathematical and statistical background is contained in the chapters and appendices. The book also provides detailed, step-by-step instructions for getting started with R and using the eRm, mirt, TAM and rstan packages for fitting Rasch models.

About the Author

Rudolf Debelak is a Senior Researcher at the University of Zurich, Switzerland. His research interests include psychometrics, with a focus on item response theory, machine learning, and the mathematical and statistical foundations of psychological research methods. Before working in academia, he was employed in the psychological test industry for several years.

Carolin Strobl is a Professor of Psychological Methods at the University of Zurich, Switzerland. Her research spans psychometrics, statistics and machine learning. She has been teaching introductory and advanced courses on statistics and psychometrics for many years and received the 2018 teaching award from her department’s student council.

Matthew Zeigenfuse currently works as a data scientist. He spent many years working in academia, researching and teaching cognitive science, psychometrics and Bayesian statistics in both the US and Switzerland.