Improving the User Experience through Practical Data Analytics

Gain Meaningful Insight and Increase Your Bottom Line
 
 
Morgan Kaufmann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 3. März 2015
  • |
  • 396 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-800678-8 (ISBN)
 

Improving the User Experience through Practical Data Analytics is your must-have resource for making UX design decisions based on data, rather than hunches. Authors Fritz and Berger help the UX professional recognize and understand the enormous potential of the ever-increasing user data that is often accumulated as a by-product of routine UX tasks, such as conducting usability tests, launching surveys, or reviewing clickstream information. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. You'll be able to more effectively delight your customers, reduce costs, improve productivity ¾ and increase your bottom line. By mastering the use of these techniques, you'll gain meaningful insight and a powerful competitive advantage for your company-and yourself.


  • 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, along with access to sample data on the companion website.


Mike Fritz has been helping businesses make their products both more usable and useful for over 20 years. An ardent proponent of the user-centered design process, he's helped to maximize the user experience for Verizon, Monster, GlaxoSmithKline, Lilly, Forrester, Rue La La, and Peoplefluent, among others. He holds a Masters in Science in Human Factors in Information Design from Bentley University.
  • Englisch
  • Saint Louis
  • |
  • USA
Elsevier Science
  • 70,39 MB
978-0-12-800678-8 (9780128006788)
0128006781 (0128006781)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Advance Praise for Improving the User Experience through Practical Data Analytics
  • Improving the User Experience through Practical Data Analytics
  • Copyright
  • Dedication
  • Contents
  • Preface
  • WHY WE WROTE THE BOOK
  • HOW THIS BOOK IS SPECIAL
  • THE SOFTWARE WE USE
  • WHAT YOU NEED TO ALREADY KNOW
  • ORGANIZATION AND COVERAGE
  • EXERCISES AND SUPPLEMENTARY MATERIAL
  • About the Authors
  • MIKE FRITZ
  • PAUL D. BERGER
  • ABOUT THE ILLUSTRATOR: RICK PINCHERA
  • Acknowledgments
  • Chapter 1 - Introduction to a variety of useful statistical ideas and techniques
  • 1.1 INTRODUCTION
  • 1.2 THE GREAT NORMAL CURVE IN THE SKY
  • 1.3 CONFIDENCE INTERVALS
  • 1.4 HYPOTHESIS TESTING
  • 1.5 SUMMARY
  • 1.6 ADDENDUM: ACTIVATING "DATA ANALYSIS"
  • REFERENCES
  • Chapter 2 - Comparing two designs (or anything else!) using independent sample T-tests
  • 2.1 INTRODUCTION
  • 2.2 CASE STUDY: COMPARING DESIGNS AT MADEMOISELLE LA LA
  • 2.3 COMPARING TWO MEANS
  • 2.4 INDEPENDENT SAMPLES
  • 2.5 MADEMOISELLE LA LA REDUX
  • 2.6 BUT WHAT IF WE CONCLUDE THAT THE MEANS AREN'T DIFFERENT?
  • 2.7 FINAL OUTCOME AT MADEMOISELLE LA LA
  • 2.8 ADDENDUM: CONFIDENCE INTERVALS
  • 2.9 SUMMARY
  • 2.10 EXERCISE
  • REFERENCE
  • Chapter 3 - Comparing two designs (or anything else!) using paired sample T-tests
  • 3.1 INTRODUCTION
  • 3.2 VIGNETTE: HOW FAST CAN YOU POST A JOB AT BEHEMOTH.COM?
  • 3.3 INTRODUCTION TO PAIRED SAMPLES
  • 3.4 EXAMPLE OF PAIRED (TWO-SAMPLE) T-TEST
  • 3.5 BEHEMOTH.COM REVISITED
  • 3.6 ADDENDUM: A MINI-DISCUSSION WHY THE INDEPENDENT AND PAIRED TESTS NEED TO BE DIFFERENT
  • 3.7 SUMMARY
  • 3.8 EXERCISE
  • REFERENCES
  • Chapter 4 - Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples
  • 4.1 INTRODUCTION
  • 4.2 CASE STUDY: IS OUR EXPENSIVE NEW SEARCH ENGINE AT BEHEMOTH.COM BETTER THAN WHAT WE ALREADY HAVE?
  • 4.3 HYPOTHESIS TESTING USING THE CHI-SQUARE TEST OF INDEPENDENCE OR FISHER'S EXACT TEST
  • 4.4 MEANWHILE, BACK AT BEHEMOTH.COM
  • 4.5 BINOMIAL CONFIDENCE INTERVALS AND THE ADJUSTED WALD METHOD
  • 4.6 SUMMARY
  • 4.7 ADDENDUM 1: HOW TO RUN THE CHI-SQUARE TEST FOR DIFFERENT SAMPLE SIZES
  • 4.8 ADDENDUM 2: COMPARING MORE THAN TWO TREATMENTS
  • 4.9 APPENDIX: CONFIDENCE INTERVALS FOR ALL POSSIBLE SAMPLE-PROPORTION OUTCOMES FROM N = 1 TO N = 15, IN TABLE A.1
  • 4.10 EXERCISES
  • REFERENCES
  • Chapter 5 - Pass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples
  • 5.1 INTRODUCTION
  • 5.3 HYPOTHESIS TESTING USING THE COCHRAN Q TEST
  • 5.4 MEANWHILE, BACK AT BACKBOARD.
  • 5.5 SUMMARY
  • 5.6 EXERCISE
  • REFERENCES
  • Chapter 6 - Comparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test
  • 6.1 INTRODUCTION
  • 6.2 CASE STUDY: SOPHISTICATED FOR WHOM?
  • 6.3 INDEPENDENT SAMPLES: ONE-FACTOR ANOVA
  • 6.4 THE ANALYSES
  • 6.5 MULTIPLE COMPARISON TESTING
  • 6.6 ILLUSTRATION OF THE S-N-K TEST
  • 6.7 APPLICATION OF THE S-N-K TO THIS RESULT
  • 6.8 DISCUSSION OF THE RESULT
  • 6.9 MEANWHILE, BACK AT MADEMOISELLE LA LA.
  • 6.10 SUMMARY
  • 6.11 EXERCISES
  • REFERENCES
  • Chapter 7 - Comparing more than two means: one factor ANOVA with a within-subject design
  • 7.1 INTRODUCTION
  • 7.2 CASE STUDY: COMPARING MULTIPLE EASE-OF-USE RATINGS AT MADEMOISELLE LA LA
  • 7.3 COMPARING SEVERAL MEANS WITH A WITHIN-SUBJECTS DESIGN
  • 7.4 HYPOTHESES FOR COMPARING SEVERAL MEANS
  • 7.5 SPSS ANALYSIS
  • 7.6 NEWMAN-KEULS ANALYSIS
  • 7.7 EXCEL ANALYSIS
  • 7.8 MADEMOISELLE LA LA: LET'S FIX THE CHECKOUT ASAP!
  • 7.9 SUMMARY
  • 7.10 EXERCISE
  • Chapter 8 - Comparing more than two means: two factor ANOVA with independent samples
  • the important role of interaction
  • 8.1 INTRODUCTION
  • 8.2 CASE STUDY: COMPARING AGE AND GENDER AT MADEMOISELLE LA LA
  • 8.3 INTERACTION
  • 8.4 WORKING THE EXAMPLE IN SPSS
  • 8.5 MEANWHILE, BACK AT MADEMOISELLE LA LA.
  • 8.6 SUMMARY
  • 8.7 EXERCISE
  • Chapter 9 - Can you relate? Correlation and simple linear regression
  • 9.1 INTRODUCTION
  • 9.2 CASE STUDY: DO RECRUITERS REALLY CARE ABOUT BOOLEAN AT BEHEMOTH.COM?
  • 9.3 THE CORRELATION COEFFICIENT
  • 9.4 LINEAR REGRESSION
  • 9.5 LINEAR REGRESSION ANALYSIS OF BEHEMOTH.COM DATA
  • 9.6 MEANWHILE, BACK AT BEHEMOTH
  • 9.7 SUMMARY
  • 9.8 ADDENDUM: A QUICK DISCUSSION OF SOME ASSUMPTIONS IMPLICIT IN INTERPRETING THE RESULTS
  • 9.9 EXERCISE
  • Chapter 10 - Can you relate in multiple ways? Multiple linear regression and stepwise regression
  • 10.1 INTRODUCTION
  • 10.2 CASE STUDY: DETERMINING THE IDEAL SEARCH ENGINE AT BEHEMOTH.COM
  • 10.3 MULTIPLE REGRESSION
  • 10.4 A CONFIDENCE INTERVAL FOR THE PREDICTION
  • 10.5 BACK TO BEHEMOTH.COM
  • 10.6 STEPWISE REGRESSION
  • 10.7 MEANWHILE, BACK AT BEHEMOTH.COM
  • 10.8 SUMMARY
  • 10.9 EXERCISE
  • Chapter 11 - Will anybody buy? Logistic regression
  • 11.1 INTRODUCTION
  • 11.2 CASE STUDY: WILL ANYBODY BUY AT THE CHARLESTON GLOBE?
  • 11.3 LOGISTIC REGRESSION
  • 11.4 LOGISTIC REGRESSION USING SPSS
  • 11.5 CHARLESTONGLOBE.COM SURVEY DATA AND ITS ANALYSIS
  • 11.6 IMPLICATIONS OF THE SURVEY-DATA ANALYSIS RESULTS-BACK TO CHARLESTONGLOBE.COM
  • 11.7 SUMMARY
  • 11.8 EXERCISE
  • Addendum: For Mac Excel Users
  • INTRODUCTION
  • CHAPTER 1
  • CHAPTER 2
  • CHAPTER 3
  • CHAPTER 4
  • CHAPTER 4
  • CHAPTER 5
  • CHAPTER 6
  • CHAPTER 7
  • CHAPTER 8
  • CHAPTER 9
  • CHAPTER 9
  • CHAPTER 10
  • CHAPTER 11
  • Index

Preface


The book will help you utilize both descriptive and predictive statistical techniques to gain meaningful insight from data collected employing traditional UX research methods, including moderated usability studies, unmoderated usability studies, surveys and contextual inquiries. However, the analytic methods we described can easily be applied to data collected in a myriad of other UX research methods, including focus groups, live Web site analytics, card sorting, competitive research, and physiological testing like eye tracking, heart rate variance, and skin conductance. This book is a how-to guide, not a treatise on statistics. We provide practical advise about which methods to use in what situations, and how to interpret the results in a meaningful way. In addition, the book provides lots of easy-to-grasp tutoring for those who have a limited knowledge of statistics. We hope the book makes many of the calculations-such as calculating a simple correlation coefficient-seem almost effortless, while providing all the necessary "hand-holding" when utilizing a more complex method, such as logistic regression.

Why We Wrote the Book


Over the past 5 years, excellent books have been published regarding collecting, analyzing, and presenting usability metrics. Arguably, the best ones in this category are the Morgan Kaufmann books, including Measuring the User Experience by Tom Tullis and Bill Albert, Beyond the Usability Lab by Bill Albert, Tom Tullis and Donna Tedesco, and Quantifying the User Experience by Jeff Sauro and James R. Lewis. These books do an outstanding job of instructing the UX professional how to choose the right metric, apply it, and effectively use the information it reveals. And yet, as we surveyed the UX research literature landscape, we saw there was currently no book that urges UX professionals to use predictive and other advanced statistical tools in their work. (The current books on usability metrics leave out the techniques often used for data analysis, such as multiple regression analysis.) But these statistical tools-which begin with basic correlation and regression analysis-are now fairly easy to access. In fact, if you have Excel, you probably have most of these tools already at your fingertips! At the same time, we recognize that many UX researchers come to the profession without formal statistical training. As a matter of fact, usability studies, contextual inquiries, surveys, and other UX research methods are sometimes performed on an ad hoc basis by designers, information architects, and front-end coders who have had no formal training in these methods, let alone training in the statistical tools used in the analysis of the data collected through such methods. Because of these realities, we start with an introductory chapter on basic statistical fundamentals. Then, we proceed gently into basic means comparison and ANOVA models. Then we move into basic correlation and more advanced regression analyses. Throughout, we strive to make techniques such as means comparisons, correlation, and regression analysis so easy to understand and apply that you will naturally turn to one of them after collecting your data. Armed with the meaning of the results, you will be able to make design decisions with authority and the backing of empirical evidence.

How This Book Is Special


We show the real-world application of these techniques through the vignettes that begin and close each chapter. By seeing parallels between the problems introduced and resolved in each chapter and your own work, you'll easily be able to ascertain the right statistical method to use in your particular UX research project. In addition, our hope is that you'll find the vignettes, and the accompanying illustrations, entertaining. All characters appearing in this work are fictitious. Any resemblance to real persons, living or dead, is purely coincidental. We provide clear insight into the statistical principles without getting bogged down in a theoretical treatise. But, we provide enough theory for you to understand why you're applying a certain technique. After all, understanding why you're doing something is just as important as knowing what you're doing. We minimize the amount of mathematical detail, while still doing full justice to the mathematical rigor of the presentation and the precision of our statements. In addition, many of our numerical examples use simple numbers. (This is a choice we consciously made, and it embraces a question posed by Ching Chun Li, Professor of Biometry at the University of Pittsburgh (1912-2003), which the authors took to heart and have incorporated into their writing: "How does one first learn to solve quadratic equations? By working with equations such as 242.5X2 - 683.1X - 19428.5 = 0, or with equations like X2 - 5X - 6 = 0?") Our belief is that simpler numerical calculations aid the readers in the intuitive understanding of the material to a degree that more than offsets any disadvantage from using numbers that don't look "real." We focus on how to get the software to do the analysis. There are a few exceptions, in those cases where Excel does not provide a built-in solution, when we show you how to use other Excel commands to duplicate, as closely as possible, what Excel would do if the technique were a built-in available one. Also, we provide end-of-chapter exercises that encourage, demonstrate, and, indeed, require the use of the statistical software described. By the way, we do not apologize for writing our chapters in a way that does not insist that the reader understand what the software is doing under the hood! We've provided additional explanatory commentary through sidebars. The information contained in the sidebars is not essential to the task of applying the analytics to the research problem at hand, but we believe they add richness to the discussion.

The Software We Use


We illustrate the use of statistical software packages with Excel and SPSS (Statistical Package for the Social Sciences). There are a large number of displays of both software packages in action. The Excel displays illustrate Excel 2007 for the PC. There is a specific module within Excel, named "Data Analysis," that needs to be activated. We show you how to perform this activation. Once you are using "Data Analysis," there is no difference at all between the Excel 2007 and Excel 2010. Since there are some minor-and not so minor-differences between the PC and Mac versions of Excel, we've provided a Mac addendum at the end of the book that shows you how to complete the same tasks step-by-step on the Mac version. Most of our displays of SPSS illustrate SPSS Edition 19. In the later chapters, we illustrate SPSS using SPSS Edition 22, the most recent version. For purposes of the techniques and analyses discussed and performed in this book, there is no meaningful difference between the two editions in how the techniques are accessed, and the resulting output format. (If you purchase SPSS, make sure that these techniques described in the book are available in your version before you buy; there are many different versions with different prices.)

What You Need to Already Know


Nothing! For the statistical beginner, we provide a chapter dedicated to some basic statistical concepts. We wrote this chapter assuming that a reader has not studied the subject matter before, but we believe that the vast majority of readers, even if they have studied the material before, will benefit from at least a cursory reading of this first chapter. The two key topics that we emphasize in the chapter are confidence intervals and hypothesis testing. We also provide some background for these two topics, centering around discussion of the bell-shaped (i.e., normal) probability distribution. A few other useful topics from a typical introductory statistics course are reviewed on an ad hoc basis. The principles and techniques discussed in this book transcend the area of their application to the UX field; the only difference from one application area to another is that different situations arise with different frequency, and correspondingly, the use of various techniques occurs with different frequency. Still, it is always helpful for people to actually see applications in their area of endeavor, and thus, we never forget that the aim is to illustrate application of the techniques in the UX area. After all, many people beginning their study of predictive analytics and statistical techniques "don't know what they don't know;" this includes envisioning the ways in which the material can be usefully applied. We assume a modest working knowledge of high school algebra. On occasion, we believe it is necessary to go a small distance beyond routine high school algebra. But, we strive to minimize the frequency of these occasions, and when it is necessary, we explain why, in the most intuitive way that we can. These circumstances exemplify how we aim to walk the fine line noted above: minimal mathematical presentation without compromising the rigor of the material or the precision of our statements.

Organization and Coverage


Our goal was to write a book that covered the most important and commonly used statistical methods employed...

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