1. SBD: Usability Evaluation
Chris North
cs3724: HCI

2. Scenario-Based Design
ANALYZE
analysis of
stakeholders,
field studies
claims about current
practice
Problem scenarios
Scenario-Based Design
DESIGN
Activity
scenarios
metaphors,
information
technology,
HCI theory,
guidelines
iterative
analysis of
usability
claims and
re-design
Information scenarios
Interaction scenarios
PROTOTYPE & EVALUATE
summative
evaluation
formative
evaluation
Usability specifications

3. Evaluation
Formative vs. Summative
Analytic vs. Emprical

4. Usability Engineering
Reqs Analysis
Design
Evaluate
Develop
many iterations

5. Usability Engineering
Formative evaluation
Summative evaluation

6. Usability Evaluation Analytic Methods: Empirical Methods:
Usability inspection, Expert review
Heuristic Evaluation
Cognitive walk-through
GOMS analysis
Empirical Methods:
Usability Testing
Field or lab
Observation, problem identification
Controlled Experiment
Formal controlled scientific experiment
Comparisons, statistical analysis

7. User Interface Metrics
Ease of learning
learning time, …
Ease of use
perf time, error rates…
User satisfaction
surveys…
Not “user friendly”

8. Usability Testing

9. Usability Testing Formative: helps guide design
Early in design process
when architecture is finalized, then its too late!
A few users
Usability problems, incidents
Qualitative feedback from users
Quantitative usability specification

10. Usability Specification Table
Scenario task
Worst case
Planned Target
Best case
(expert)
Observed
Find most expensive house for sale?
1 min.
10 sec.
3 sec.
??? sec …

11. Usability Test Setup Set of benchmark tasks Consent forms
Easy to hard, specific to open-ended
Coverage of different UI features
E.g. “find the 5 most expensive houses for sale”
Different types: learnability vs. performance
Consent forms
Not needed unless video-taping user’s face (new rule)
Experimenters:
Facilitator: instructs user
Observers: take notes, collect data, video tape screen
Executor: run the prototype if faked
Users
3-5 users, quality not quantity

12. Usability Test Procedure
Goal: mimic real life
Do not cheat by showing them how to use the UI!
Initial instructions
“We are evaluating the system, not you.”
Repeat:
Give user a task
Ask user to “think aloud”
Observe, note mistakes and problems
Avoid interfering, hint only if completely stuck
Interview
Verbal feedback
Questionnaire
~1 hour / user

13. Usability Lab
E.g. McBryde 102

14. Data Note taking Verbal protocol: think aloud
E.g. user keeps clicking on the wrong button…”
Verbal protocol: think aloud
E.g. user thinks that button does something else…
Rough quantitative measures
HCI metrics: e.g. task completion time, ..
Interview feedback and surveys
Video-tape screen & mouse
Eye tracking, biometrics?

15. Analyze Initial reaction: Mature reaction: Identify usability problems
“stupid user!”, “that’s developer X’s fault!”, “this sucks”
Mature reaction:
“how can we redesign UI to solve that usability problem?”
the user is always right
Identify usability problems
Learning issues: e.g. can’t figure out or didn’t notice feature
Performance issues: e.g. arduous, tiring to solve tasks
Subjective issues: e.g. annoying, ugly
Problem severity: critical vs. minor

16. Cost-Importance Analysis
Importance 1-5: (task effect, frequency)
5 = critical, major impact on user, frequent occurance
3 = user can complete task, but with difficulty
1 = minor problem, small speed bump, infrequent
Ratio = importance / cost
Sort by this
3 categories: Must fix, next version, ignored
Problem
Importance
Solutions
Cost
Ratio I/C

17. Refine UI Simple solutions vs. major redesigns
Solve problems in order of: importance/cost
Example:
Problem: user didn’t know he could zoom in to see more…
Potential solutions:
Better zoom button icon, tooltip
Add a zoom bar slider (like moosburg)
Icons for different zoom levels: boundaries, roads, buildings
NOT: more “help” documentation!!! You can do better.
Iterate
Test, refine, test, refine, test, refine, …
Until? Meets usability specification

18. Project: Usability Evaluation
>=3 users: Not (tainted) HCI students
Simple data collection (Biometrics optional!)
Exploit this opportunity to improve your design
Report:
Procedure (users, tasks, specs, data collection)
Usability problems identified, specs not met
Design modifications

19. Controlled Experiments

20. Usability test vs. Controlled Expm.
Formative: helps guide design
Single UI, early in design process
Few users
Usability problems, incidents
Qualitative feedback from users
Controlled experiment:
Summative: measure final result
Compare multiple UIs
Many users, strict protocol
Independent & dependent variables
Quantitative results, statistical significance

21. What is Science?
Measurement
Modeling

22. Scientific Method Form Hypothesis Collect data Analyze
Accept/reject hypothesis
How to “prove” a hypothesis in science?
Easier to disprove things, by counterexample
Null hypothesis = opposite of hypothesis
Disprove null hypothesis
Hence, hypothesis is proved

23. Empirical Experiment Typical question: Spotfire vs. TableLens
Which visualization is better in which situations?
Spotfire vs. TableLens

24. Cause and Effect Goal: determine “cause and effect” Procedure:
Cause = visualization tool (Spotfire vs. TableLens)
Effect = user performance time on task T
Procedure:
Vary cause
Measure effect
Problem: random variation
Cause = vis tool OR random variation?
random variation
Real world
Collected data
uncertain conclusions

25. Stats to the Rescue Goal: Hypothesis: Null hypothesis: Stats: Hence:
Measured effect unlikely to result by random variation
Hypothesis:
Cause = visualization tool (e.g. Spotfire ≠ TableLens)
Null hypothesis:
Visualization tool has no effect (e.g. Spotfire = TableLens)
Hence: Cause = random variation
Stats:
If null hypothesis true, then measured effect occurs with probability < 5% (e.g. measured effect >> random variation)
Hence:
Null hypothesis unlikely to be true
Hence, hypothesis likely to be true

26. Variables
Independent Variables (what you vary), and treatments (the variable values):
Visualization tool
Spotfire, TableLens, Excel
Task type
Find, count, pattern, compare
Data size (# of items)
100, 1000,
Dependent Variables (what you measure)
User performance time
Errors
Subjective satisfaction (survey)
HCI metrics

27. Example: 2 x 3 design n users per cell Task1 Task2 Task3 Spot-fire
Ind Var 2: Task Type
Task1
Task2
Task3
Spot-fire
Table-Lens
Ind Var 1: Vis. Tool
Measured user performance times (dep var)

28. Groups “Between subjects” variable
1 group of users for each variable treatment
Group 1: 20 users, Spotfire
Group 2: 20 users, TableLens
Total: 40 users, 20 per cell
“With-in subjects” (repeated) variable
All users perform all treatments
Counter-balancing order effect
Group 1: 20 users, Spotfire then TableLens
Group 2: 20 users, TableLens then Spotfire
Total: 40 users, 40 per cell

29. Issues Eliminate or measure extraneous factors Randomized Fairness
Identical procedures, …
Bias
User privacy, data security
IRB (internal review board)

30. Procedure For each user: * n users Sign legal forms
Pre-Survey: demographics
Instructions
Do not reveal true purpose of experiment
Training runs
Actual runs
Give task
measure performance
Post-Survey: subjective measures
* n users

31. Data Measured dependent variables Spreadsheet: User Spotfire TableLens
task 1
task 2
task 3

32. Step 1: Visualize it Dig out interesting facts Qualitative conclusions
Guide stats
Guide future experiments

33. Step 2: Stats Task1 Task2 Task3 Spot-fire 37.2 54.5 103.7 Table-Lens
Ind Var 2: Task Type
Task1
Task2
Task3
Spot-fire
37.2
54.5
103.7
Table-Lens
29.8
53.2
145.4
Ind Var 1: Vis. Tool
Average user performance times (dep var)

34. TableLens better than Spotfire?
Problem with Averages: lossy
Compares only 2 numbers
What about the 40 data values? (Show me the data!)
Avg Perf time
(secs)
Spotfire TableLens

35. The real picture Need stats that compare all data Avg Perf time (secs)
Spotfire TableLens

36. Statistics t-test ANOVA: Analysis of Variance Result:
Compares 1 dep var on 2 treatments of 1 ind var
ANOVA: Analysis of Variance
Compares 1 dep var on n treatments of m ind vars
Result:
p = probability that difference between treatments is random (null hypothesis)
“statistical significance” level
typical cut-off: p < 0.05
Hypothesis confidence = 1 - p

37. In Excel

38. p < 0.05 Woohoo! Found a “statistically significant” difference
Averages determine which is ‘better’
Conclusion:
Cause = visualization tool (e.g. Spotfire ≠ TableLens)
Vis Tool has an effect on user performance for task T …
“95% confident that TableLens better than Spotfire …”
NOT “TableLens beats Spotfire 95% of time”
5% chance of being wrong!
Be careful about generalizing

39. p > 0.05 Hence, no difference? NOT! How?
Vis Tool has no effect on user performance for task T…?
Spotfire = TableLens ?
NOT!
Did not detect a difference, but could still be different
Potential real effect did not overcome random variation
Provides evidence for Spotfire = TableLens, but not proof
Boring, basically found nothing
How?
Not enough users
Need better tasks, data, …

40. Data Mountain
Robertson, “Data Mountain” (Microsoft)

41. Data Mountain: Experiment
Data Mountain vs. IE favorites
32 subjects
Organize 100 pages, then retrieve based on cues
Indep. Vars:
UI: Data mountain (old, new), IE
Cue: Title, Summary, Thumbnail, all 3
Dependent variables:
User performance time
Error rates: wrong pages, failed to find in 2 min
Subjective ratings

42. Data Mountain: Results
Spatial Memory!
Limited scalability?