Human-Computer Interaction: An Empirical Research Perspective

by I. Scott MacKenzie

Human-Computer An Empirical Research Perspective is the definitive guide to empirical research in HCI. The book begins with foundational topics including an historical context, the human factor, interaction elements, and the fundamentals of science and research. From there, readers will progress to learning about the methods for conducting an experiment to evaluate a new computer interface or interaction technique. There are detailed discussions and how-to analyses on models of interaction, focusing on descriptive models and predictive models. Writing and publishing a research paper is explored with helpful tips for success.

Throughout the book, readers will find hands-on exercises, checklists, and real-world examples. This is a must-have, comprehensive guide to empirical and experimental research in HCI – an essential addition to your HCI library.

Provides a master, A-to-Z guide in a concise, hands-on referencePresents the practical and theoretical ins-and-outs of user studiesIncludes exercises, takeaway points, and case studies throughoutUpdated to incorporate developments in HCI, including Human performance outliers, Interaction pointing and selecting; text input; gesture input

Genres: Design, Research, Academic, Science, Artificial Intelligence, Unfinished

451 pages, Kindle Edition

First published January 1, 2012

About the author

I. Scott MacKenzie is Associate Professor of Computer Science and Engineering at York University, Canada. For the past 25 years, MacKenzie has been an active member of the human-computer interaction (HCI) research community, with over 130 peer-reviewed publications, including more than 30 papers in the Association for Computing Machinery Conference on Human Factors in Computing Systems (ACM SIGCHI) conference proceedings. MacKenzie’s interests include human performance measurement and modeling, interaction devices and techniques, text entry, mobile computing, accessible computing, touch-based interaction, eye tracking, and experimental methodology.

Reviews

[Fatma Moawad · Rating 4 out of 5]

I love this book. I read many books in HCI and this is definitely my pick for any of my classes. It addresses the reality that I took so much time to discover. It gives quick guidance with enough details for a good understanding. Definitely recommended for those who are empirical in how they do science and as a text book for an intro HCI course.

[Paulina Durán · Rating 5 out of 5]

Very easy to read and with cool examples, I definitely think this is a great book to understand and be able to carry out a small research :)

[Carter · Rating 2 out of 5]

This book is probably a 3-4 star, depending on the audience. The problem is, empirical research methods, are well known to me. A young field, some ideas are mentioned on how to characterize interactions with a GUI.

[Ark · Rating 5 out of 5]

An excellent, condensed review of HCI research, the methodologies, and history.

[Grant · Rating 4 out of 5]

No-nonsense and informative intro to HCI, written cleanly and clearly. Unlike many textbooks in this area, or computing in general, it isn't dated either (as of 2016), as there are many examples using mobile, recent research, etc. I found chapter 3 on interaction elements the most useful, followed by chapter 5 on designing experiments. Within-subjects vs between-subjects experiment design was interesting. Also internal vs external validity. If you've read Don Norman's stuff, you'll notice a lot of it here, but this textbook goes beyond the theory of heuristics, metaphor, mental models, etc. to discuss using theory in actual experiments. Chapter 8 is only really relevant to students (I no longer am one). The textbook also could have benefited from colour in the images, but I guess the printers took the cheap route (my edition, at least, was all greyscale/black and white).

"The point is that a user expectation is broken. Broken expectations sooner or later cause errors." pg. 112

[Jim Bumpus · Rating 4 out of 5]

Goes beyond usability testing to answer "How do we collect scientific data to answer questions about user interfaces?"