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.

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