Before I started to read this book, I searched for the authors’ definition of user experience (UX). To my surprise, I did not find one. Of course, I may have missed it; however, if it is really missing, then this is definitely a weak aspect of the book. The reason I started with this search is because many people have issues with UX, usability, and related concepts. These terms are often used without scoping or defining them, which diminishes the value of work conducted in this field.

For the purposes of this review, I will adopt the International Organization for Standardization (ISO) 9241-210 definition of UX: “A person’s perceptions and responses that result from the use or anticipated use of a product, system, or service” (http://www.iso.org/iso/catalogue_detail.htm?csnumber=52075). This comes quite close to the book’s definition of user research: “the systematic study of the goals, needs, and capabilities of users so as to specify design, construction, or improvement of tools to benefit how users work and live” (p. 9). However, although related, these definitions do differ. The definition of user research includes the definition of UX. This makes for an odd start to the book. Fortunately, some resources available online provide more information on the ins and outs of UX [1,2,3]. These sources can help readers get a solid footing for reading this book.

Many books have already been published on UX, some even by the same publisher [4,5,6]. Where Buxton’s book [4] emphasizes the qualitative aspects of UX, Tullis and Albert [5] and Goodman et al. [6] focus on quantitative aspects of UX, as this book does. However, they focus on data acquisition and not on data analysis, which is the prime focus here. This is what makes this book valuable for both students and practitioners. Both these categories of readers can also benefit from a complementary book by the same authors [7]. This book provides more hands-on computing practice, examples, and exercises to give readers a jump-start on quantifying UX.

The book takes a pragmatic and realistic--hence, the best possible--approach. In chapter 1, the authors provide four decision trees that help the practitioner or student choose the right chapter to start with. It even provides pointers up to section level for the methods denoted in the decision trees, so there are no more excuses for making a mistake when choosing your quantitative analysis design. Having said this, it should be noted that the book provides an overview of rather basic statistical analysis and does not touch advanced statistical analysis (for example, mixed models and structural equation models). As such, the book provides room for follow-up books to continue where it leaves off.

Without going into too much depth, I can say that the authors provide the basic mathematics underlying their analysis. For those that lack the fundamentals of statistics, the authors provide an appendix with a nutshell overview. Reading this book requires high-school-level mathematics, nothing more, which makes it accessible to a very wide audience. On the other hand, those already in the field can use it as a refresher. Moreover, each chapter takes a historical stance, providing sufficient explanations and a decent list of references.

I will admit that I had a false start with this book. I noticed its weak spot--perhaps its only one--from the beginning. However, taken as a whole, it provides a pragmatic approach to quantifying UX, without oversimplifying or claiming too much. It delivers what it promises. This book is valuable for both practitioners and students, in virtually any discipline. It can help psychologists transfer their statistical knowledge to UX practice, practitioners quickly assess their envisioned design and analysis, engineers demystify UX, and students appreciate UX’s merits.