Measuring the User Experience Collecting, Analyzing, and Presenting Usability Metrics

ISBN-10: 0124157815

ISBN-13: 9780124157811

Edition: 2nd 2013

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Description:

Measuring the User Experience was the first book that focused on how to quantify the user experience. Now in the second edition, the authors include new material on how recent technologies have made it easier and more effective to collect a broader range of data about the user experience. As more UX and web professionals need to justify their design decisions with solid, reliable data, Measuring the User Experience provides the quantitative analysis training that these professionals need. The second edition presents new metrics such as emotional engagement, personas, keystroke analysis, and net promoter score. It also examines how new technologies coming from neuro-marketing and online market research can refine user experience measurement, helping usability and user experience practitioners make business cases to stakeholders. The book also contains new research and updated examples, including tips on writing online survey questions, six new case studies, and examples using the most recent version of Excel. Learn which metrics to select to measure for every case, including behaviorial, physiological, audio, persuasion, aesthetics measuring, as well as gestural, verbal, and physicalVendor-neutral examination of user experience with web sites, digital products, and other physical user experiencesIn-depth global case studies show how organizations have successfully used presented metrics and the information they revealedCompanion site, www.measuringux.com, inclides articles, tools, spreadsheets, presentations, and other resources to help you effectively measure the user experience
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Book details

Edition: 2nd
Copyright year: 2013
Publisher: Elsevier Science & Technology
Publication date: 7/16/2013
Binding: Paperback
Pages: 320
Size: 7.50" wide x 9.00" long x 1.00" tall
Weight: 1.518
Language: English

Tom Tullis is Vice President of Usability and User Insight at Fidelity Investments and Adjunct Professor at Bentley University in the Human Factors in Information Design program. He joined Fidelity in 1993 and was instrumental in the development of the company's usability department, including a state-of-the-art Usability Lab. Prior to joining Fidelity, he held positions at Canon Information Systems, McDonnell Douglas, Unisys Corporation, and Bell Laboratories. He and Fidelity's usability team have been featured in a number of publications, including Newsweek , Business 2.0 , Money , The Boston Globe , The Wall Street Journal , and The New York Times.

Preface to the Second Edition
Acknowledgments
Biographies
Introduction
What Is User Experience
What Are User Experience Metrics?
The Value of UX Metrics
Metrics for Everyone
New Technologies in UX Metrics
Ten Myths about UX Metrics
Metrics Take Too Much Time to Collect
UX Metrics Cost Too Much Money
UX Metrics Are Not Useful When Focusing on Small Improvements
UX Metrics Don't Help Us Understand Causes
UX Metrics Are Too Noisy
You Can Just Trust Your Gut
Metrics Don't Apply to New Products
No Metrics Exist for the Type of Issues We Are Dealing with
Metrics Are not Understood or Appreciated by Management
It's Difficult to Collect Reliable Data with a Small Sample Size
Background
Independent and Dependent Variables
Types of Data
Nominal Data
Ordinal Data
Interval Data
Ratio Data
Descriptive Statistics
Measures of Central Tendency
Measures of Variability
Confidence Intervals
Displaying Confidence Intervals as Error Bars
Comparing Means
Independent Samples
Paired Samples
Comparing More Than Two Samples
Relationships Between Variables
Correlations
Nonparametric Tests
The x<sup>2</sup> Test
Presenting your Data Graphically
Column or Bar Graphs
Line Graphs
Scatterplots
Pie or Donut Charts
Stacked Bar or Column Graphs
Summary
Planning
Study Goals
Formative Usability
Summative Usability
User Goals
Performance
Satisfaction
Choosing the Right Metrics: Ten Types of Usability Studies
Completing a Transaction
Comparing Products
Evaluating Frequent Use of the Same Product
Evaluating Navigation and/or Information Architecture
Increasing Awareness
Problem Discovery
Maximizing Usability for a Critical Product
Creating an Overall Positive User Experience
Evaluating the Impact of Subtle Changes
Comparing Alternative Designs
Evaluation Methods
Traditional (Moderated) Usability Tests
Online (Unmoderated) Usability Tests
Online Surveys
Other Study Details
Budgets and Timelines
Participants
Data Collection
Data Cleanup
Summary
Performance Metrics
Task Success
Binary Success
Levels of Success
Issues in Measuring Success
Time on Task
Importance of Measuring Time on Task
How to Collect and Measure Time on Task
Analyzing and Presenting Time-on-Task Data
Issues to Consider When Using Time Data
Errors
When to Measure Errors
What Constitutes an Error?
Collecting and Measuring Errors
Analyzing and Presenting Errors
Issues to Consider When Using Error Metrics
Efficiency
Collecting and Measuring Efficiency
Analyzing and Presenting Efficiency Data
Efficiency as a Combination of Task Success and Time
Learnability
Collecting and Measuring Learnability Data
Analyzing and Presenting Learnability Data
Issues to Consider When Measuring Learnability
Summary
Issue-Based Metrics
What Is a Usability Issue?
Real Issues versus False Issues
How to Identify an Issue
In-Person Studies
Automated Studies
Severity Ratings
Severity Ratings Based on the User Experience
Severity Ratings Based on a Combination of Factors
Using a Severity Rating System
Some Caveats about Rating Systems
Analyzing and Reporting Metrics for Usability Issues
Frequency of Unique Issues
Frequency of Issues Per Participant
Frequency of Participants
Issues by Category
Issues by Task
Consistency in Identifying Usability Issues
Bias in Identifying Usability Issues
Number of Participants
Five Participants Is Enough
Five Participants Is Not Enough
Our Recommendation
Summary
Self-Reported Metrics
Importance of Self-Reported Data
Rating Scales
Likert Scales
Semantic Differential Scales
When to Collect Self-Reported Data
How to Collect Ratings
Biases in Collecting Self-Reported Data
General Guidelines for Rating Scales
Analyzing Rating-Scale Data
Post-Task Ratings
Ease of Use
After-Scenario Questionnaire (ASQ)
Expectation Measure
A Comparison of Post-task Self-Reported Metrics
Postsession Ratings
Aggregating Individual Task Ratings
System Usability Scale
Computer System Usability Questionnaire
Questionnaire for User Interface Satisfaction
Usefulness, Satisfaction, and Ease-of-Use Questionnaire
Product Reaction Cards
A Comparison of Postsession Self-Reported Metrics
Net Promoter Score
Using SUS to Compare Designs
Online Services
Website Analysis and Measurement Inventory
American Customer Satisfaction Index
OpinionLab
Issues with Live-Site Surveys
Other Types of Self-Reported Metrics
Assessing Specific Attributes
Assessing Specific Elements
Open-Ended Questions
Awareness and Comprehension
Awareness and Usefulness Gaps
Summary
Behavioral and Physiological Metrics
Observing and Coding Unprompted Verbal Expressions
Eye Tracking
How Eye Tracking Works
Visualizing Eye-Tracking Data
Areas of Interest
Common Eye-Tracking Metrics
Eye-Tracking Analysis Tips
Pupillary Response
Measuring Emotion
Affectiva and the Q-Sensor
Blue Bubble Lab and Emovision
Seren and Emotiv
Stress and Other Physiological Measures
Heart Rate Variance
Heart Rate Variance and Skin Conductance Research
Other Measures
Summary
Combined and Comparative Metrics
Single Usability Scores
Combining Metrics Based on Target Goals
Combining Metrics Based on Percentages
Combining Metrics Based on Z Scores
Using Single Usability Metric
Usability Scorecards
Comparison to Goals and Expert Performance
Comparison to Goals
Comparison to Expert Performance
Summary
Special Topics
Live Website Data
Basic Web Analytics
Click-Through Rates
Drop-Off Rates
A/B Tests
Card-Sorting Data
Analyses of Open Card-Sort Data
Analyses of Closed Card-Sort Data
Tree Testing
Accessibility Data
Return-On-Investment Data
Summary
Case Studies
Net Promoter Scores and the Value of a Good User Experience
Methods
Results
Prioritizing Investments in Interface Design
Discussion
Conclusion
References
Biographies
Measuring the Effect of Feedback on Fingerprint Capture
Methodology
Discussion
Conclusion
Acknowledgment
References
Biographies
Redesign of a Web Experience Management System
Test Iterations
Data Collection
Workflow
Results
Conclusions
Biographies
Using Metrics to Help Improve a University Prospectus
Example 1: Deciding on Actions after Usability Testing
Example 2: Site-Tracking Data
Example 3: Triangulation for Iteration of Personas
Summary
Acknowledgments
References
Biographies
Measuring Usability Through Biometrics
Background
Methods
Biometric Findings
Qualitative Findings
Conclusions and Practitioner Take-Aways
Acknowledgments
References
Biographies
Ten Keys to Success
Make Data Come Alive
Don't Wait to Be Asked to Measure
Measurement Is Less Expensive Than You Think
Plan Early
Benchmark Your Products
Explore Your Data
Speak the Language of Business
Show Your Confidence
Don't Misuse Metrics
Simplify Your Presentation
References
Index
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