Improving the User Experience through Practical Data Analytics

Gain Meaningful Insight and Increase Your Bottom Line

Mike Fritz, Paul D. Berger

• Publisher: Morgan Kaufmann
• Year: 2015
• Print ISBN:
  9780128006351
• eBook ISBN:
  9780128006788
• Topic: Design
• Subtopic: Elaborazione di dati

Improving the User Experience through Practical Data Analytics shows you how to make UX design decisions based on data—not hunches. Authors Fritz and Berger help the UX professional recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you'll delight your users, increase your bottom line and gain a powerful competitive advantage for your company—and yourself. Key features include: - Practical advise on choosing the right data analysis technique for each project. - A step-by-step methodology for applying each technique, including examples and scenarios drawn from the UX field. - Detailed screen shots and instructions for performing the techniques using Excel (both for PC and Mac) and SPSS. - Clear and concise guidance on interpreting the data output. - Exercises to practice the techniques - Practical guidance on choosing the right data analysis technique for each project. - Real-world examples to build a theoretical and practical understanding of key concepts from consumer and financial verticals. - A step-by-step methodology for applying each predictive technique, including detailed examples. - A detailed guide to interpreting the data output and examples of how to effectively present the findings in a report. - Exercises to learn the techniques

Table of contents

• Cover image
• Title page
• Table of Contents
• Advance Praise for Improving the User Experience through Practical Data Analytics
• Copyright
• Dedication
• Preface
• About the Authors
• Acknowledgments
• Chapter 1. Introduction to a variety of useful statistical ideas and techniques
• Chapter 2. Comparing two designs (or anything else!) using independent sample T-tests
• Chapter 3. Comparing two designs (or anything else!) using paired sample T-tests
• Chapter 4. Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples
• Chapter 5. Pass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples
• Chapter 6. Comparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test
• Chapter 7. Comparing more than two means: one factor ANOVA with a within-subject design
• Chapter 8. Comparing more than two means: two factor ANOVA with independent samples; the important role of interaction
• Chapter 9. Can you relate? Correlation and simple linear regression
• Chapter 10. Can you relate in multiple ways? Multiple linear regression and stepwise regression
• Chapter 11. Will anybody buy? Logistic regression
• Addendum: For Mac Excel Users
• Index