Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics (Interactive Technologies)

Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics (Interactive Technologies)

Thomas Tullis, William Albert

Language: English

Pages: 336

ISBN: 0123735580

Format: PDF / Kindle (mobi) / ePub


Effectively measuring the usability of any product requires choosing the right metric, applying it, and effectively using the information it reveals. Measuring the User Experience provides the first single source of practical information to enable usability professionals and product developers to do just that. Authors Tullis and Albert organize dozens of metrics into six categories: performance, issues-based, self-reported, web navigation, derived, and behavioral/physiological. They explore each metric, considering best methods for collecting, analyzing, and presenting the data. They provide step-by-step guidance for measuring the usability of any type of product using any type of technology.

• Presents criteria for selecting the most appropriate metric for every case
• Takes a product and technology neutral approach
• Presents in-depth case studies to show how organizations have successfully used the metrics and the information they revealed

 

 

 

 

 

 

 

 

 

 

 

 

 

the middle of the study so their task times will be unusually large. Also, some participants may have taken an impossibly short amount of time to complete the task. This is likely an indicator that they were not truly engaged in the study. Some general rules for how to filter time data are included in Section 4.2. You should also consider filtering out data for participants who do not reflect your target audience or where outside factors impacted the results. We’ve had more than a few usability

issue to consider is whether to use a concurrent think-aloud protocol when collecting time data (i.e., asking participants to think aloud while they are going through the tasks). Most usability specialists rely heavily on a concurrent think-aloud protocol to gain important insight into the user experience. But sometimes a think-aloud protocol leads to a tangential topic or a lengthy interaction with the moderator. The last thing you want to do is measure time on task while a participant is giving

condition. Error bars represent the 95% confidence interval for the mean. Adapted from Tedesco and Tullis (2006); used with permission. The key finding was that one of the five conditions, Condition 1 resulted in better correlations starting at the smallest sample sizes and continuing. Even at a sample size of only seven, which is typical of many usability tests, its correlation with the full data set averaged 0.91, which was significantly higher than any of the other conditions. So Condition 1,

easily and quickly. 11. The information (such as online help, on-screen messages, and other documentation) provided with this system was clear. 12. It was easy to find the information I needed. 13. The information provided for the system was easy to understand. 14. The information was effective in helping me complete the tasks and scenarios. 15. The organization of information on the system screens was clear. 16. The interface of this system was pleasant. 17. I liked using the interface of

measurements: how our weight changes when we step on the bathroom scale, where to set our thermostat in the evening, and how to interpret our water bill every month. The user experience field is no different. We have a set of metrics specific to our profession: task success, user satisfaction, and errors, among others. This book gathers all the UX metrics in one place and explains how to use these metrics to provide maximum benefit to you and your organization. So what is a UX metric and how does

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