Quantifying the User Experience

Practical Statistics for User Research
 
 
Morgan Kaufmann (Verlag)
  • 2. Auflage
  • |
  • erschienen am 12. Juli 2016
  • |
  • 350 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-12-802548-2 (ISBN)
 

Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website?

This book provides a foundation for statistical theories and the best practices needed to apply them. The authors draw on decades of statistical literature from human factors, industrial engineering, and psychology, as well as their own published research, providing both concrete solutions (Excel formulas and links to their own web-calculators), along with an engaging discussion on the statistical reasons why tests work and how to effectively communicate results. Throughout this new edition, users will find updates on standardized usability questionnaires, a new chapter on general linear modeling (correlation, regression, and analysis of variance), with updated examples and case studies throughout.


  • Completely updated to provide practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices
  • Includes new and revised information on standardized usability questionnaires, as well as general linear modeling (correlation, regression, and analysis of variance)
  • Shows practitioners which test to use, why they work, and best practices for application, along with easy-to-use Excel formulas and web-calculators for analyzing data
  • Recommends ways for researchers and practitioners to communicate results to stakeholders in plain English


Dr. Jeff Sauro is a six-sigma trained statistical analyst and founding principal of MeasuringU, a customer experience research firm based in Denver. For over fifteen years he's been conducting usability and statistical analysis for companies such as Google, eBay, Walmart, Autodesk, Lenovo and Drobox or working for companies such as Oracle, Intuit and General Electric.
Jeff has published over twenty peer-reviewed research articles and five books, including Customer Analytics for Dummies. He publishes a weekly article on user experience and measurement online at measuringu.com.
Jeff received his Ph.D in Research Methods and Statistics from the University of Denver, his Masters in Learning, Design and Technology from Stanford University, and B.S. in Information Management & Technology and B.S. in Television, Radio and Film from Syracuse University. He lives with his wife and three children in Denver, CO.
  • Englisch
  • San Francisco
  • |
  • USA
Elsevier Science
  • 15,82 MB
978-0-12-802548-2 (9780128025482)
0128025484 (0128025484)
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  • Cover
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Biographies
  • Foreword
  • Preface to the Second Edition
  • Acknowledgments
  • Chapter 1 - Introduction and how to use this book
  • Introduction
  • The organization of this book
  • How to use this book
  • What test should I use?
  • What sample size do I need?
  • You don't have to do the computations by hand
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 2 - Quantifying user research
  • What is user research?
  • Data from user research
  • Usability testing
  • Sample sizes
  • Representativeness and randomness
  • Three types of studies for user research
  • Data collection
  • Completion rates
  • Usability problems (UI problems)
  • Task Time
  • Errors
  • Satisfaction ratings
  • Combined scores
  • A/B testing
  • Clicks, page views, and conversion rates
  • Survey data
  • Rating scales
  • Net Promoter Scores
  • Comments and open-ended data
  • Requirements gathering
  • Key points
  • References
  • Chapter 3 - How precise are our estimates? Confidence intervals
  • Introduction
  • Confidence interval = twice the margin of error
  • Confidence intervals provide precision and location
  • Three components of a confidence interval
  • Confidence level
  • Variability
  • Sample size
  • Confidence interval for a completion rate
  • Confidence interval history
  • Wald interval: terribly inaccurate for small samples
  • Exact confidence interval
  • Adjusted-Wald: add two successes and two failures
  • Best point estimates for a completion rate
  • Guidelines on reporting the best completion rate estimate
  • How accurate are point estimates from small samples?
  • Confidence interval for a problem occurrence
  • Confidence interval for rating scales and other continuous data
  • Confidence interval for task-time data
  • Mean or median task time?
  • Variability
  • Bias
  • Geometric mean
  • Computing the geometric mean
  • Log transforming confidence intervals for task-time data
  • Confidence interval for large sample task times
  • Confidence interval around a median
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 4 - Did we meet or exceed our goal?
  • Introduction
  • One-tailed and two-tailed tests
  • Comparing a completion rate to a benchmark
  • Small sample test
  • Mid-probability
  • Large sample test
  • Comparing a satisfaction score to a benchmark
  • Do at least 75% agree? converting continuous ratings to discrete
  • Disadvantages to converting continuous ratings to discrete
  • Net Promoter Score
  • Comparing a task time to a benchmark
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 5 - Is there a statistical difference between designs?
  • Introduction
  • Comparing two means (rating scales and task times)
  • Within-subjects comparison (paired t-test)
  • Confidence interval around the difference
  • Practical significance
  • Comparing task times
  • Normality assumption of the paired t-test
  • Between-subjects comparison (two-sample t-test)
  • Confidence interval around the difference
  • Assumptions of the t-tests
  • Normality
  • Equality of variances
  • Don't worry too much about violating assumptions (except representativeness)
  • Comparing completion rates, conversion rates, and A/B testing
  • Between-subjects
  • Chi-square test of independence
  • Small sample sizes
  • Two-proportion test
  • Fisher exact test
  • Yates correction
  • N-1 Chi-square test
  • N-1 Two-proportion test
  • Confidence interval for the difference between proportions
  • Within-subjects
  • McNemar exact test
  • Concordant pairs
  • Discordant pairs
  • Alternate approaches
  • Chi-square statistic
  • Yates correction to the chi-square statistic
  • Confidence interval around the difference for matched pairs
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 6 - What sample sizes do we need? Part 1: summative studies
  • Introduction
  • Why do we care?
  • The type of usability study matters
  • Basic principles of summative sample size estimation
  • Estimating values
  • Comparing values
  • What can I do to control variability?
  • Sample size estimation for binomial confidence intervals
  • Binomial sample size estimation for large samples
  • Binomial sample size estimation for small samples
  • Sample size for comparison with a benchmark proportion
  • Sample size estimation for chi-squared tests (independent proportions)
  • Sample size estimation for McNemar Exact Tests (matched proportions)
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 7 - What sample sizes do we need? Part 2: formative studies
  • Introduction
  • Using a probabilistic model of problem discovery to estimate sample sizes for formative user research
  • The famous equation P(x = 1) = 1 - (1 - p)n
  • Deriving a sample size estimation equation from 1 - (1 - p)n
  • Using the tables to plan sample sizes for formative user research
  • Assumptions of the binomial probability model
  • Additional applications of the model
  • Estimating the composite value of p for multiple problems or other events
  • Adjusting small-sample composite estimates of p
  • Estimating the number of problems available for discovery and the number of undiscovered problems
  • What affects the value of p?
  • What is a reasonable problem discovery goal?
  • Reconciling the "magic number five" with "eight is not enough"
  • Some history-the 1980s
  • Some more history-the 1990s
  • The derivation of the "Magic Number 5"
  • Eight is not enough-a reconciliation
  • More about the binomial probability formula and its small-sample adjustment
  • The origin of the binomial probability formula
  • How does the deflation adjustment work?
  • Other statistical models for problem discovery
  • Criticisms of the binomial model for problem discovery
  • Expanded binomial models
  • Capture-recapture models
  • Why not use one of these other models when planning formative user research?
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 8 - Standardized usability questionnaires
  • Introduction
  • What is a standardized questionnaire?
  • Advantages of standardized usability questionnaires
  • What standardized usability questionnaires are available?
  • Assessing the quality of standardized questionnaires: reliability, validity, and sensitivity
  • Other item characteristics
  • Number of scale steps
  • Availability of a neutral response
  • Agreement versus bipolar scales
  • Norms
  • Post-study questionnaires
  • QUIS (Questionnaire for User Interaction Satisfaction)
  • Description of the QUIS
  • Psychometric evaluation of the QUIS
  • SUMI (Software Usability Measurement Inventory)
  • Description of the SUMI
  • Psychometric evaluation of the SUMI
  • PSSUQ (Post-Study System Usability Questionnaire)
  • Description of the PSSUQ
  • Psychometric evaluation of the PSSUQ
  • PSSUQ norms and interpretation of normative patterns
  • SUS (System Usability Scale)
  • Description of the SUS
  • Psychometric evaluation of the SUS
  • SUS norms
  • Does it hurt to be positive? evidence from an alternate form of the SUS
  • UMUX (Usability Metric for User Experience)
  • Description of the UMUX
  • Psychometric evaluation of the UMUX
  • UMUX-LITE
  • Description of the UMUX-LITE
  • Psychometric evaluation of the UMUX-LITE
  • Experimental comparison of Post-Study usability questionnaires
  • Post-task questionnaires
  • ASQ (After-Scenario Questionnaire)
  • Description of the ASQ
  • Psychometric evaluation of the ASQ
  • SEQ (Single Ease Question)
  • Description of the SEQ
  • Psychometric evaluation of the SEQ
  • SMEQ (Subjective Mental Effort Question)
  • Description of the SMEQ
  • Psychometric evaluation of the SMEQ
  • ER (Expectation Ratings)
  • Description of expectation ratings
  • Psychometric evaluation of expectation ratings
  • UME (Usability Magnitude Estimation)
  • Description of UME
  • Psychometric evaluation of UME
  • Experimental comparisons of POST-TASK questionnaires
  • Questionnaires for assessing perceived usability of websites
  • WAMMI (Website Analysis and Measurement Inventory)
  • Description of the WAMMI
  • Psychometric evaluation of the WAMMI
  • SUPR-Q (Standardized User Experience Percentile Rank Questionnaire)
  • Description of the SUPR-Q
  • Psychometric evaluation of the SUPR-Q
  • Other questionnaires for assessing websites
  • Other questionnaires of interest
  • CSUQ (Computer System Usability Questionnaire)
  • USE (Usefulness, Satisfaction, and Ease-of-Use)
  • HQ (hedonic quality)
  • EMO (emotional metric outcomes)
  • ACSI (American customer satisfaction index)
  • NPS (Net Promoter Score)
  • CxPi (Forrester customer experience index)
  • TAM (technology acceptance model)
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 9 - Six enduring controversies in measurement and statistics
  • Introduction
  • Is it OK to average data from multipoint scales?
  • On one hand
  • On the other hand
  • Our recommendation
  • Do you need to test at least 30 users?
  • On one hand
  • On the other hand
  • Our recommendation
  • Should you always conduct a two-tailed test?
  • On one hand
  • On the other hand
  • Our recommendation
  • Can you reject the null hypothesis when p > 0.05?
  • On one hand
  • On the other hand
  • Our recommendation
  • Can you combine usability metrics into single scores?
  • On one hand
  • On the other hand
  • Our recommendation
  • What if you need to run more than one test?
  • On one hand
  • On the other hand
  • Our recommendation
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Chapter 10 - An introduction to correlation, regression, and ANOVA
  • Introduction
  • Correlation
  • How to compute a correlation
  • Statistical significance of r
  • Confidence intervals for r
  • Interpreting the magnitude of r
  • Sample size estimation for r
  • Coefficient of determination (R2)
  • Correlation with binary data
  • Computing the Phi correlation
  • Regression
  • Estimating slopes and intercepts
  • Confidence intervals for slopes and predicted values
  • Confidence interval for the slope
  • Confidence interval for predicted values (including the intercept)
  • Sample size estimation for linear regression
  • Slope
  • Intercept
  • Analysis of variance
  • Comparing more than two means
  • How the omnibus test works
  • Multiple comparisons
  • Assessing interactions
  • Confidence intervals and sample size estimation for ANOVA
  • Confidence intervals
  • Sample size estimation
  • Key points
  • Chapter review questions
  • Answers to chapter review questions
  • References
  • Appendix: derivation of sample size formulas for regression
  • Based on confidence interval for regression slope
  • Based on confidence interval for regression intercept
  • Chapter 11 - Wrapping up
  • Introduction
  • Getting more information
  • Good luck!
  • Key points
  • References
  • Appendix - A crash course in fundamental statistical concepts
  • Introduction
  • Types of data
  • Populations and samples
  • Sampling
  • Measuring central tendency
  • Mean
  • Median
  • Geometric mean
  • Standard deviation and variance
  • The normal distribution
  • z-scores
  • Area under the normal curve
  • Applying the normal curve to user research data
  • Central Limit Theorem
  • Standard error of the mean
  • Margin of error
  • t-distribution
  • Significance testing and p-values
  • How much do sample means fluctuate?
  • The logic of hypothesis testing
  • Errors in statistics
  • Key points
  • Subject Index
  • Back cover

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