Book Review: Generalized Kernel Equating with Applications in R WibergMarieGonzalezJorgevon DavierAlina A. (2025). Generalized Kernel Equating with Applications in R. Boca Raton, Florida. CRC Press, p. 272. Hardback £71.99, eBook £39.99. ISBN: Hardba

The measurement of individual characteristics that are not directly observable is one of the core objectives of psychometrics, which accomplishes this task by making use of advanced statistical methodologies.These features are inspected using standardized tests, which are purposefully developed to gather evidence of them.However, these tests can be composed of a variety of different items, leading to the issue of the comparability of the results across the forms of the test.There are several reasons for developing various versions of a test.In educational assessment, it is important to avoid the use of items that have become known to test takers.In fact, the overexposure of the items can pose issues about item security, while subjects tested several times might memorize the questions.Nevertheless, tests might consist of different items also because these instruments have been developed in diverse contexts or studies.This explains the need for a procedure to obtain comparable test scores across different forms of a test, irrespective of the difficulty of the items included.Equating pursues this objective, and its importance is demonstrated by the substantial scientific literature addressing this topic.The book Generalized Kernel Equating with Applications in R makes a meaningful contribution to this body of the literature.The book is particularly focused on the kernel method, a statistical procedure to obtain smooth functions and applied in the context of equating to achieve continuous score distributions.With respect to a previous book on kernel equating (von Davier, Holland, & Thayer, 2004), the newly published work expands the approach proposing a general scheme where each step of the procedure can be carried out employing various methods.The broad review of proposals in the literature is encapsulated in this framework, thus offering a better comprehension of the full process.Hence, a wide range of options is proposed for each step, leaving the possibility of developing new methods within this general scheme.In the book, the full process is referred to as the generalized kernel equating (GKE) framework, due to the clear structure outlined and the variety of options included for each step.This book is organized into four parts.The first part contains two chapters on introductory concepts in test equating and the fundamental phases of the GKE framework.The second part includes six chapters and describes in detail each step, offering a wide range of options for each one.Two other chapters are included in the third part, which shows how to apply the methods to real datasets.Finally, the fourth part contains two appendices about the R software used for applications.Chapter 1 explains the purpose of the equating process and introduces the approach, followed throughout the book, of considering it as a statistical procedure.It defines the adopted notation, illustrates the various data collection designs, and explains the function that maps the scores