Eye Tracking the User Experience. Aga Bojko

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       PART IV: ANALYSIS AND REPORTING

       CHAPTER 10

       Data Extraction and Preparation

       Setting Fixation Criteria

       Drawing Areas of Interest (AOIs)

       Extracting Measures and Exporting Data

       Cleansing Data

       Don’t Jump the Gun

       Summary

       CHAPTER 11

       Eye Tracking Data Visualizations

       Classification of Visualizations

       Gaze Plots / Scanpaths

       Gaze Videos

       Bee Swarms

       Heatmaps and Focus Maps

       Dynamic Heatmaps

       Summary

       CHAPTER 12

       Qualitative Data Analysis

       Visualizations for Qualitative Analysis

       No Hard-and-Fast Rules, Sorry!

       Target Search Analysis Framework

       Step 1: Define Success

       Step 2: Determine the Search Outcome

       Step 3: Analyze Failures to Explain Why They Happened

       Step 4: Analyze Successes to Detect Potential Issues

       What About More Open-Ended Tasks?

       Qualitative Analysis of Comprehension Tasks

       Summary

       CHAPTER 13

       Quantitative Data Analysis

       Select Measures Early

       Compare, Compare, Compare

       Fun with Inferential Statistics

       Graphing the Data

       Painting a Picture

       Describing the Findings

       Structuring the Report to Tell a Story

       Unrealized Potential of Quantitative Eye Tracking

       Summary

       Index

       Figure Credits

       Acknowledgments

       About the Author

FOREWORD

      Eye tracking has always seemed very promising. After all, in our profession we spend a lot of our time trying to read users’ minds. One of our most powerful tools—usability testing—consists simply of asking users to think aloud while they use our products so that we can understand where they’re getting confused. I describe it as trying to see the thought balloons forming over users’ heads, especially the balloons that have question marks in them. They’re the ones that tell us what needs fixing.

      Eye tracking is naturally appealing to us because it holds out the promise of another window into the mind: the semi-magical ability to know what people are looking at. And since there seems to be a strong (though not absolute) connection between what people are looking at and what they’re paying attention to, eye tracking can provide another set of useful clues about what they’re thinking and why the product is confusing them.

      At the very least, it promises to answer questions like “Did they even see that big button that says ‘Download?’” If the eye tracking shows that people aren’t seeing it, then we know that they can’t possibly act on it, and we should probably give some thought to making it more prominent somehow.

      That’s why eye tracking is one of the three technologies I’ve been waiting for, for a long time.¹ In fact, when I wrote Don’t Make Me Think, it was going to be based in part on some eye tracking research I was going to do. But it turned out that the technology at the time—particularly the software to analyze the mountains of data that eye trackers produce—just СКАЧАТЬ