You're being asked to quantify usability improvements with statistics. But even with a background in statistics, you are hesitant to statistically analyze the data, as you may be unsure about which statistical tests to use and have trouble defending the use of the small test sample sizes associated with usability studies. The book is about providing a practical guide on how to use statistics to solve common quantitative problems arising in user research. It addresses common questions you face every day such as: Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on the 1st attempt? How long will it take users to purchase products on the website? This book shows you which test to use, and how provide a foundation for both the statistical theory and best practices in applying them. The authors draw on decades of statistical literature from Human Factors, Industrial Engineering and Psychology, as well as their own published research to provide the best solutions. They provide both concrete solutions (excel formula, links to their own web-calculators) along with an engaging discussion about the statistical reasons for why the tests work, and how to effectively communicate the results. Provides practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practicesShow practitioners which test to use, why they work, best practices in application, along with easy-to-use excel formulas and web-calculators for analyzing dataRecommends ways for practitioners to communicate results to stakeholders in plain English
Tough reading: this book requires strong statistics background to be fully understandable and useful to the reader. Good entry point for those who are looking for pointers into the quantitative side of user research. The main controversies regarding user research methods are robustly addressed, mostly based on recent developments in statistics that support ex. the validity of small samples in usability testing. There's also a good overview and comparison of questionnaires.
I missed a more practical and condensed set of guidelines for statistics laymen that one could use directly in everyday tasks.
This book provides well-written instruction on the practical aspects of quantitative analysis in user research. Having a course or two of college-level stats is helpful for understanding the content. I appreciated the examples, the history and background for some of the conventions, and I was glad to see some controversial statistical issues handled in a very even-handed way. Also--I hope you like reading about confidence intervals because the authors evidently love writing about them (and calculating them, too).
I hate statistics, so my rating of this book would be 0/5. But that's a silly thing to do. I just read this entire book for class and feel like it should count towards my Goodreads total. >:(
It's a shame that I was just about finished my certificate program in applied stat when this came out. It's really all I needed to know to add great quantitative analysis to my UX research!
It's fairly decent, nice reference points for measuring user experience, but it is very math-y. So depends on the reality of what you need in a workplace, and how much you want to emphasise quantitative measures of success versus qualitative measures in your organisation.
In the reality of most organisations I've worked in, it's mostly qual, and in as far as you get with quant, you can do with the HEART framework.
I think is a good reference book to have in the bookshelf when you know that you will have to measure success in metrics... and perhaps if your role is focused on pure User Research.
I think some of these things mayyy just be a bit old school, but still very useful!
Very good book on using statistics in usability. I don't have a lot of knowledge / experience in usability testing (so no opinion on that), but the book gives a very good overview of the statistical methods that usability researchers should use (and unfortunately often don't).
I also liked the numerous reminders about the formulas and what the Greek letters represent (Really useful to have reminders on what everything means when you don't work with stats / data analysis in a research setting or on a constant basis).
If you haven't taken or grasped some decent Stats courses, you will still learn but not as much as you would with understanding of statistics. Will also most likely require second reading for most of us.