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9781138710467

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Overall, the book has a lot of great detail and is technically sound. It is also clearly written and at an appropriate level of difficulty. Another big strength is the chapters with R applications. I know my students would love this, and generally this is the type of guidance they want with respect to conducting IRT in R, as opposed to trying to field through the different packages, figure out how they scale latent variables in the package, and more. A final big strength is the focus on fairness as a core underlying issue of model evaluation. From the Introduction chapter and throughout the various other chapters, issues of fairness were put at the forefront. This aligns with the Standards for Educational and Psychological Testing, and in general with modern views of validity and test theory. - Anne Corinne Huggins-Manley. University of Florida With regard to R, I think it provides a good introduction. The manuscript explains the basic features of R code and then proceeds to give many examples of R in action , which I think it a good approach. Overall, the manuscript is very well-written. Most of the explanations of Rasch model properties or R commands are extraordinary clear. - Leah Feuerstahler, Fordham University I feel that the book focuses mostly on the Rasch model - this is reflected more in the Applications section, where most of the examples demonstrated are with Rasch models. Compared with other books introducing IRT, this book starts with a thorough introduction to the simplest model (the Rasch model) followed by estimation methods, information function and model-data fit measures. I find it useful that the book summarizes the existing R packages for different purposes of fitting IRT models. - Yanyan Sheng, University of Chicago

Overall, the book has a lot of great detail and is technically sound. It is also clearly written and at an appropriate level of difficulty. Another big strength is the chapters with R applications. I know my students would love this, and generally this is the type of guidance they want with respect to conducting IRT in R, as opposed to trying to field through the different packages, figure out how they scale latent variables in the package, and more. A final big strength is the focus on fairness as a core underlying issue of model evaluation. From the Introduction chapter and throughout the various other chapters, issues of fairness were put at the forefront. This aligns with the Standards for Educational and Psychological Testing, and in general with modern views of validity and test theory. - Anne Corinne Huggins-Manley. University of Florida With regard to R, I think it provides a good introduction. The book explains the basic features of R code and then proceeds to give many examples of R in action , which I think it a good approach. Overall, the book is very well-written. Most of the explanations of Rasch model properties or R commands are extraordinary clear. - Leah Feuerstahler, Fordham University

Author Information

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.

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