Evaluating Usability

1. Evaluating Usability Data & Analysis

2. Types of Data And how to read them

3. What types of data are relevant to our interests? • When evaluating how usable a design is, there are many data you may want to take into account: whether a user can complete a task, how long it takes them to complete a task, survey responses, etc. • But before thinking about what data you will collect, you must understand the basic types of data that exist and what they can tell you about your interface.

4. Nominal Data • Unordered groups or categories (e.g. apples and oranges; no fruit is inherently better) • Examples: • Binary success (whether a user was or was not able to complete a task) • Some demographic information, such as gender or whether or not the participant owns a smartphone • But not others, such as age or annual household income

5. Ordinal Data • Ordered groups or categories • Examples: • Survey rankings • “Would you describe this website as excellent, good, fair, or poor?” • Levels of task completion (for non-binary success) • If I tell you to draw a circle and you draw an oval, shouldn’t you get partial credit?

6. Caveat for Ordinal Data “Would you describe this website as excellent, good, fair, or poor?” • Although good is better than fair and fair is better than poor, we have no way of knowing whether the “distance” between good and fair is greater than the “distance” between fair and poor • You cannot do arithmetic with ordinal data, but you can summarize it with histograms. OR ? FAIR FAIR GOOD GOOD POOR POOR

7. Interval Data • Data points which are measured along a scale where each point is equidistant from one another • Examples: • 5-star ratings such as those used by Yelp, Google Local, etc. • Semantic differentials: “Would you describe these slides as… Ugly □ □ □ □ □ Beautiful”

8. Caveat for Interval Data • You cannot multiply or divide interval data. • There is no way something can be “twice as beautiful” or “three times as ugly” because there is no meaningful zero point.

9. Ordinal vs. Interval • Interval data provides more opportunity for analysis than nominal or ordinal data do, but the scales used often look the same: □ Poor □ Fair □ Good □ Excellent Vs. Poor □ □ □ □ Excellent • How do these different formats affect your participants’ responses? How do they effect what you can and cannot do with the data?

10. Working with Ordinal and Interval Data • Because ordinal data is not uniformly distributed along its scale, we cannot treat it like interval data • Remember, you cannot do any arithmetic on ordinal data • This means no averaging! • It may seem pedantic, but you cannot treat the response “fair” the same way as “1/3 of the way between poor and excellent”

11. Ratio Data • Like interval data, but with the addition of an inherently meaningful absolute zero • Examples: • Time to task completion • Number of page views, mouse clicks, etc.

12. Interval vs. Ratio Data • The concept of an absolute zero means you can do any type of arithmetic you like with ratio data • You can make relative statements • You can say that one participant took twice as much time to complete a task as another, but you can’t say one movie is twice as good as another • You can take the geometric mean • Jakob Nielsen believes this is important, because it prevents a single big number from skewing the result and it accounts fairly for cases in which some of the metrics are negative

13. Evaluating a UI • There are qualities you may wish to gather user data for: performance and satisfaction • Performance data is typically “hard data” relating to how easily a UI can be used to accomplish a given set of tasks; e.g. • Percentage of successfully completed tasks • Average time to task completion • Number of clicks-through • Satisfaction data covers emotional response to a UI and is generally self-reported; e.g. • How aesthetically pleasing the UI was • How easy to use the participant found the UI vs. how easy they thought the task was

14. Performance vs. Satisfaction • How much data you gather about each of those qualities will depend on what type of a system you are building • If you are building a stock-trading application to be used in-house by Goldman Sachs, chances are you are about performance more than satisfaction • If you are building a social game for Zynga, chances are you will care more about satisfaction • Does performance imply satisfaction? • Sometimes, a bit of speed can be sacrificed for better user experience (e.g. iPhone animations)

15. Performance metrics Measuring success and efficiency

16. Task success • To measure success, you must first define a clear desired end-state for your task • “Find the current price of a share of GOOG stock” (clear end-state) • “Research ways to save for retirement” (unclear end-state) • Binary success– tasks which are necessarily pass/fail • Levels of success – tasks which may be partially completed or completed in less-than-optimal ways

17. Looking at levels of success • Let’s say you are evaluating the GIMP interface and one of your tasks is having participants draw a circle • What are the different possible levels of success for this task? • Where is the cutoff for failure?

18. Issues in Measuring Success • Deciding what constitutes success • Deciding when to end a task if the participant is not successful • Tell participants to stop trying at the point where, in the real world, they would give up or seek assistance • Allow participants a certain number of attempts to complete the task • Issue: What constitutes an “attempt”? • Set a time limit

19. Time-On-Task • This data can be analyzed in a number of ways: • Looking at the median or geometric mean (typically less skewed than the mean) • Creating ranges to report frequency of users falling into each interval • Create a threshold which models the “acceptable” amount of time to complete a particular task • Look at distribution to identify outliers—especially important for remote testing, which often yields “noisy” data (e.g., a participant goes and gets a sandwich halfway through a task)

20. Issues in Measuring Time-On-Task • Should you include time on unsuccessful tasks? • How will including or throwing out this data affect your results? • Will asking users to voice their thoughts while completing the task alter their time to completion? • Will voicing thoughts aloud cause users to complete tasks more quickly/slowly than they would otherwise? • Quantitative methods are only part of the goal for usability testing. The voice-aloud method can provide you with useful qualitative data. • Should you tell participants that their time until completion is being measured?

21. Efficiency • Most people think of efficiency as equivalent to time-on-task • Efficiency can also be considered a measurement of the amount of effort required to complete a task. • Effort is a quantification of the number of actions a user takes (e.g. mouse clicks, page views, keystrokes, etc.) • To measure efficiency, you must define what your units of meaningful action are and what the precise start and endpoints are for your task • Typically, effort is only calculated for successfully completed tasks

22. Lostness • Another measure of efficiency, especially important for websites, is that of “lostness”, which can be modeled using the following formula:

23. Learnability • We have already discussed learning curves in class • You hope that, the more times a user has used your UI, the less time it will take them to complete a task and the less effort it will require • In this sense, we can model learnability as time-on-task, success rate, and/or efficiency over time

24. Self-Reported Metrics “These slides are AWESOME!!!11!1”

25. The Importance of Self-Reported Data • So far, we have focused mostly on data which is gathered by observing the user • However, it is sometimes necessary to gather data directly from the user • This is especially common when you are looking to evaluate user satisfaction rather than performance

26. Likert Scales • Likert scales consist of a statement, either positive or negative, followed by a 5-point scale of agreement • Example: “I found this website easy to use.” □ Strongly disagree □ Disagree □ Neither agree nor disagree □ Agree □ Strongly agree • This is an example of ordinal data!

27. Semantic Differential Scales • These are similar to Likert scales, but run on scales anchored by opposing adjectives • Example: “I would describe this website as…” Ugly □ □ □ □ □ Beautiful • This is an example of interval data!

28. Expectation Measure • Some experts (Albert and Dixon 2003) believe that the best way to assess the ease or difficulty of a given task is relative to how easy or difficult the participant thought it was going to be • Thus, for each task you might ask participants to rate both how easy/difficult they thought it would be and how easy/difficult it actually was • Doing so allows you to calibrate what is otherwise essentially an arbitrary measure • Remember, individuals may have different ideas of how “easy” and “very easy” compare!

29. Post-Task vs. Post-Session Evaluation • It is generally helpful to chunk your lab time into a number of tasks • You can gather self-reported data either after each task, after the entirety of the session, or both • The previously described methods can be used either post-task or post-session

30. System Usability Scale • A System Usability Scale can be used for post-session self-reporting • There are many variants on this test. Here is a classic example:

31. Other Types of Self-Reporting • Self-reported metrics can also be used to evaluate specific elements (e.g. navigation bar) or overall attributes (e.g. visual appeal) of a UI • When evaluating elements, it is helpful to examine gaps between awareness and usefulness • To do this, you might ask, “Were you aware of this functionality prior to this study? (Yes/No)” followed by “On a scale of 1 to 5, how useful is this functionality to you? (1=Not at all useful; 5=Very useful)