Improving the User Experience through Practical Data Analytics (eBook)

Gain Meaningful Insight and Increase Your Bottom Line

Paul D. Berger, Mike Fritz (Autoren)

eBook Download: PDF | EPUB

2015 | 1. Auflage
396 Seiten
Elsevier Science (Verlag)
978-0-12-800678-8 (ISBN)

Infos

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

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.

Textauszug

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...

Produkt-Details

• Erscheint lt. Verlag: 3.3.2015
• Sprache: englisch
• Themenwelt: Informatik ► Software Entwicklung ► User Interfaces (HCI)
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• ISBN-10: 0-12-800678-1 / 0128006781
• ISBN-13: 978-0-12-800678-8 / 9780128006788