sample size (ch 7) Y Binary data? . . . . . The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. . . . . . . . . . . . . . . In fact, we know many practitioners who find the field appealing because it largely avoids those impersonal numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Finally, at “Task Time?,” take the “Y” path, which leads you to “1-Sample t (Log).” As shown in Table 1.1, you’ll find that method discussed in Chapter 4 in the “Comparing a Task Time to a Benchmark” section on p. 54. . . . To get started finding Quantifying The User Experience Practical Statistics For User Research , you are right to find our website which has a comprehensive collection of manuals listed. . . . . . . . . . . . 105 Basic Principles of Summative Sample Size Estimation.... ..... ..... ..... 106 Estimating Values. . . . . . . . . . . N − 1 two- proportion proportion test and Fisher exact test (ch 5) Y N N (ch 5) Y Adjusted Large sample? . . 288 Key Points from the Appendix...... ...... ...... ...... ...... ...... ...... ..... 289 Index..... ....... ...... ....... ...... ....... ...... ....... ...... ....... ...... ...... ....... . . . . . . . . . . . . . . . . . . . . . . . . 160 Some More History: The 1990s. Many designers and researchers view usability and design as qualitative activities, which do not require attention to formulas and numbers. . . . . . . . . . . . . 7 Key Points from the Chapter. . . . . . 248 On One Hand. . Sep 30, 2020 quantifying the user experience practical statistics for user research Posted By Jir? . Suppose you have some conversion-rate data and you just want to understand how precise the estimate is. . . . . To get an estimate of precision you compute a confidence interval around your sample metrics (e.g., what is the margin of error around a completion rate of 70%; see Chapter 3). . . . . . . ........ ........ ........ ........ ........ ........ . . . . . . . . . . . . in each group N N N N Y Adjusted Chi Wald square Testing against a 3 or more groups? . . . . . . . . . . . . . eBook includes PDF, ePub and Kindle version. . . ........ ........ ........ ........ ........ ........ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. . . . . . . . . . . . . . . . . . . . . . ..... ...... ..... ..... ..... ...... ..... ..... ..... ...... . . . Quantifying the User Experience: Practical Statistics for User Research was published by master.sasongko on 2016-02-20. . . . . . . . . . . . . . . . . 185 Advantages of Standardized Usability Questionnaires. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Which sample size estimation method should you use? . ...... ....... ...... ... 190 PSSUQ (Post-study System Usability Questionnaire). . Although design and several usability activities are certainly qualitative, the impact of good and bad designs can be easily quantified in conversions, completion rates, completion times, perceived satisfaction, recommendations, and sales. . . . . . . . . . . ..... ..... ..... ..... ..... ..... ...... ..... ..... .... 66 Between-subjects Comparison (Two-sample t-test). . . . . . . 178 References....... ....... ....... ....... ....... ...... ....... ....... ....... ..... 181 CHAPTER 8 Standardized Usability Questionnaires. . . . . 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. . . . . in Television, Radio and Film from Syracuse University. . Like this book? Some of you may have never taken a statistics course whereas others probably took several in graduate school. . . . . . It includes both standard statistical output (p-values and confidence intervals) and some more user- friendly output that, for example, reminds you how to interpret that ubiquitous p-value and that you can paste right into reports. . . . . . . Continuing on that path, the final decision depends on whether there are two groups to compare or more than two groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I thank my wife, Cathy, for her patience and loving support. multiple in each group 2-sample t (ch 5, 9, 10) N N N ANOVA or Testing against a 3 or more groups? . . quantifying the user experience practical statistics for user research Sep 18, 2020 Posted By Dean Koontz Ltd TEXT ID e704f3fa Online PDF Ebook Epub Library amazons book store everyday low prices and free delivery on eligible orders jeff has published over fifteen peer reviewed research articles and presents tutorials and papers . . . . . 1 How to Use This Book....... ............ ............. ............ ............. 2 What Test Should I Use?. 250 Can you Reject the Null Hypothesis when p > 0.05?. . . . . . . . . . . At the “Comparing Groups?” box, select “Y” because there will be two groups of data, one for each product. . . . . . . . . . . . . . . . . 63 Comparing Task Times. . . . . . 143 Using a Probabilistic Model of Problem Discovery to Estimate Sample Sizes for Formative User Research. Notices Knowledge and best practice in this field are constantly changing. . . . . . . Conversion-rate data are binary-discrete, so start with the decision map in Figure 1.2. Table 1.3 Chapter Sections for Methods Depicted in Figures 1.3 and 1.4 Method Chapter: Section [Page] 2 Proportions 6: Sample Size Estimation for Chi-Square Tests (Independent Proportions) [128] 2 Means 6: Comparing Values—Example 6 [116] Paired Proportions 6: Sample Size Estimation for McNemar Exact Tests (Matched Proportions) [131] Paired Means 6: Comparing Values—Example 5 [115] Proportion to Criterion 6: Sample Size for Comparison with a Benchmark Proportion [125] Mean to Criterion 6: Comparing Values—Example 4 [115] Margin of Error Proportion 6: Sample Size Estimation for Binomial Confidence Intervals [121] Margin of Error Mean 6: Estimating Values—Examples 1–3 [112] Problem Discovery Sample Size 7: Using a Probabilistic Model of Problem Discovery to Estimate Sample Sizes for Formative User Research [143]. . . . I am fortunate to have a family that puts up with my obsessions. . . . . . . . . . . . . . . . We also provide: Background chapters with an overview of common ways to quantify user research (Chapter 2) • and a quick introduction/review of many fundamental statistical concepts (Appendix). . . . . . . Instead, this book is about working backwards from the most common questions and problems you’ll encounter as you conduct, analyze, and report on user research projects. . . . . . . . . . . . . . . . . . . . . . . . . . . The first step is to identify the type of test for which you’re collecting data. . . . .... ... ... ... .... ... ... .... ... ... ... .... ... ... 276 z-scores. . . . . . . . . . . . Often the first collision a user researcher has with statistics is in planning sample sizes. . 2. . . . . . . . . . . . . . 121 Binomial Sample Size Estimation for Large Samples.... ......... ........ . . . . Prior to Stanford, he received his B.S. . . . . . . . . . . . . . . He lives with his wife and three children in Denver, CO. Dr. James R. (Jim) Lewis is a senior human factors engineer (at IBM since 1981) with a current focus on the design and evaluation of speech applications and is the author of Practical Speech User Interface Design. . . . . . . . . . . . . . . . . . . . . . Suppose you’re planning to run a formative usability study—one where you’re goingtowatch people use the product you’re developing and see what problems they encounter. . . . . . . . . . Some people want to see how the statistics work, and for them we provide the math. . . Many designers and researchers view usability and design as qualitative activities, which do … . . . . . . . . . . . . . . . . . . . . . . . . . . . What Users Do: Top Task Analysis. . . . . . . . . Quantifying the User Experience: Practical Statistics for User Research offers a practical guide for using statistics to solve quantitative problems in user research. . . . . . . . . Problem discovery, specifically the number of users you need in a usability test to find a specified percentage of usability problems with a specified probability of occurrence. . . . . . . . . . . . McNemar Wald confidence exact test Y N interval (ch 5) (ch 3) Adjusted Wald confidence 1-sample 1-sample interval for z-test binomial difference in (ch 4) (ch 4) matched proportions (ch 5) FIGURE 1.2 Decision map for analysis of discrete-binary data (e.g., completion rates or conversion rates). . . . 3 or more groups? . . . . . . . 276 The Normal Distribution. . . . . . . . . Another part is due to our selecting the best procedures for practical user research, focusing on procedures that work well for the types of data and sample sizes you’ll likely encounter. . . . . 2. . . . We’ve created an Excel calculator that performs all the computations covered in this book. . . . . . . . . . . . . . . . . . . . . . . . . 161 The Derivation of the “Magic Number 5”. . . . . Problem Estimating a N discovery parameter? . . . . . . . . . . . . . . . . References Lewis, J.R., Sauro, J., 2012. . . .... .... .... .... .... .... .... .... .... .... .... ..... .... .... .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... .... ..... .... .... ... 19 Confidence Intervals Provide Precision and Location....................... 19 Three Components of a Confidence Interval. . . 2. . . . . . . Thank you to my family for the patience and encouragement through the process. Part of this is from recent advances in statistics (especially for dealing with binary data). . . . . . . . . . . . . . . Y multiple benchmark? . . . . . . . . . . . .... .... ... .... .... .... ... .... .... .... ... .... .... .... ... 174 Key Points from the Chapter. . . . . . . The top 20% of U.S, taxpayers pay 68% of all taxes [pdf] Here are three examples of how the Pareto Principle applies to user research. . . The top 10% of cell phone users consume 90% of wireless bandwidth. . . 3. . . . . . 120 Sample Size Estimation for Binomial Confidence Intervals. . . . . . . . . . . . My friends are so mad that they do not know how I have all the high quality ebook which they do not! . . . . 15 Rating Scales...... ...... ...... ...... ....... ...... ...... ...... ...... ...... 15 Net Promoter Scores. . . . . . . . . Many thanks. . . . . . . . . . . . . . . . . . . . It is available for purchase online at www.measuringusability.com/ products/expandedStats. . . . . . . . . . . . . . . . . . . . . 188 QUIS (Questionnaire for User Interaction Satisfaction). . . . . . . . . ........ ........ ........ ....... ........ ...... 30 Geometric Mean..... ..... ...... ...... ..... ...... ...... ...... ..... ...... .. 31 Confidence Interval for Large Sample Task Times. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Introduction. . . . . . See Example 6 on p. 116. . . . . . To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. . . . . . . . . . . . . . 8 CHAPTER 2 Quantifying User Research. . . . . . . . . . . . . . . . . . . This leads you to “Problem Discovery Sample Size,” which, according to Table 1.3, is discussed in Chapter 7 in the “Using a Probabilistic Model of Problem Discovery to Estimate Sample Sizes for Formative User Research” section on p. 143. . . 123 Sample Size for Comparison with a Benchmark Proportion. . . 1 Introduction.............. ......................... ......................... .... 1 The Organization of This Book. . . . . . . . . . ...... ...... ..... .... 21 Exact Confidence Interval. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeff received his Masters in Learning, Design and Technology from Stanford University with a concentration in statistical concepts. . . . . . . . . . . . . . ..... ..... ...... ..... ..... 242 On One Hand. . . . . Suppose you need to analyze a sample of task-time data against a specified benchmark. . 262 References....... ....... ....... ....... ....... ...... ....... ....... ....... ..... 266 CHAPTER 10 Wrapping Up.... ........ ........ ......... ........ ........ ......... ....... 269 Introduction. . . . . . . . . 15 Clicks, Page Views, and Conversion Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Through the process not only am I satisfied with the answers I’ve found but also with what I’ve learned and the people whom I’ve met, most notably my co-author Jim Lewis. . . ........ ........ ........ ..... 24 Confidence Interval for a Problem Occurrence. . . . . 146 Assumptions of the Binomial Probability Model. . . . We have made it easy for you to find a PDF Ebooks without any digging. . . . . . . .... ... .... ... .... 254 On One Hand. . . ..... ...... ..... ..... ...... ..... .... 33 Key Points from the Chapter. . . . . . . . . . . . . . . Because rating-scale data are not binary, select “N” at the “Binary Data?” box. . . . . . . . . . . . . . . . . xv CHAPTER 1 Introduction and How to Use This Book. . . Many designers and researchers view usability and design as qualitative activities, which do not require attention to formulas and numbers. . . 149 Adjusting Small Sample Composite Estimates of p. .... .... ... .... .... .... 149 Estimating the Number of Problems Available for Discovery and the Number of Undiscovered Problems. . . . . . . . . Summarizing data and computing margins of error (Chapter 3). . . . . . . . . . . . . 254 On the Other Hand. . . [256] 10: Getting More Information [269] Table 1.2 Chapter Sections for Methods Depicted in Figure 1.2 Method Chapter: Section [Page] One-Sample z-Test 4: Comparing a Completion Rate to a Benchmark (Large Sample Test) [49] One-Sample Binomial 4: Comparing a Completion Rate to a Benchmark (Small Sample Test) [45] Adjusted Wald Confidence Interval 3: Adjusted-Wald Interval: Add Two Successes and Two Failures [22] McNemar Exact Test 5: McNemar Exact Test [84] Adjusted Wald Confidence Interval for 5: Confidence Interval around the Difference for Matched Difference in Matched Proportions Pairs [89] N − 1 Two-Proportion Test and Fisher 5: N − 1 Two-Proportion Test [79]; Fisher Exact Test [78] Exact Test Adjusted Wald Difference in Proportion 5: Confidence for the Difference between Proportions [81] Chi-Square 10: Getting More Information [269] For example, let’s say you want to know which statistical test to use if you are comparing com- pletion rates on an older version of a product and a new version where a different set of people par- ticipated in each test. . . . . xv. . . . . . . . . . . . . . . . . . . . . . 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. . See the Appendix for a discussion of the distinction between discrete and continuous data. . . . Readers who are familiar with many statistical procedures and formulas may find that some of the formulas we use differ from what you learned in your college statistics courses. . . . . . . . . . .... .... .... .... ..... .... .... .... .... .... ..... ... 9 What is User Research?. . . . . . . . . . . . . . . . . . . . 17 References....... ............... ............... ................ ............... 17 vii, Contents viii CHAPTER 3 How Precise Are Our Estimates? . . 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. . . . He is a regular presenter and instructor at the Computer Human Inter- action (CHI) and Usability Professionals Associations (UPA) conferences. . . . . . . .... .... .... .... .... .... .... .... .... .... .... ... 278 Applying the Normal Curve to User Research Data. . . . . . . . . . . . . . ........ ........ ........ ........ ........ ........ ....... 13 Task Time.... ....... ........ ....... ........ ....... ....... ........ ....... . . . . . . . . . . . . . . . . CHAPTER 1 Introduction and How to Use This Book 8 Answers 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . You’re just estimating the rate rather than comparing a set of rates, so at “Comparing Data?,” take the “N” path. . . . . . . . . . . . . 246 On One Hand. . . . . . . . . . . . ........ ........ ....... ........ ..... 213 SEQ (Single Ease Question). 273 Types of Data. . . Quantifying the User Experience Practical Statistics for User Research Jeff Sauro James R. Lewis AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier, Acquiring Editor: Steve Elliot Development Editor: Dave Bevans Project Manager: Jessica Vaughan Designer: Joanne Blank Morgan Kaufmann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA © 2012 Jeff Sauro and James R. Lewis.
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