1. Professor John Canny Spring 2003
CS 160: Lecture 16
Professor John Canny
Spring 2003
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2. Outline More on user testing Basics of quantitative methods
Choosing participants
Designing the test
Basics of quantitative methods
Collecting data
Analyzing the data
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3. Why do User Testing? Can’t tell how good or bad UI is until?
people use it!
Other methods are based on evaluators who?
may know too much
may not know enough (about tasks, etc.)
Summary
Hard to predict what real users will do
Jakob Nielsen
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4. Choosing Participants
Representative of eventual users in terms of
job-specific vocabulary / knowledge
tasks
If you can’t get real users, get approximation
system intended for doctors
get medical students
system intended for electrical engineers
get engineering students
Use incentives to get participants
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5. Incentives Paying every subject a fixed amount can be too expensive.
A small chance at a large reward is a good incentive – e.g. subjects get a 1/n chance to receive an MP3 player.
Offer to tell them the results of the study.
Free software for software companies.
Some courses require participation as research subjects.
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6. Ethical Considerations
Sometimes tests can be distressing
users have left in tears (embarrassed by mistakes)
You have a responsibility to alleviate
make voluntary with informed consent
avoid pressure to participate
will not affect their job status either way
let them know they can stop at any time
stress that you are testing the system, not them
make collected data as anonymous as possible
Get human subjects approval if needed – typically if results are going to be published.
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7. User Test Proposal A report that contains
objective
description of system being testing
task environment & materials
participants
methodology
tasks
test measures
Get approved & then reuse for final report
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8. Qualitative vs. Quantitative Studies
Qualitative: What we’ve been doing so far:
Contextual Inquiry: trying to understand user’s tasks and their conceptual model.
Usability Studies: looking for critical incidents in a user interface.
In general, we use qualitative methods to:
Understand what’s going on, look for problems, or get a rough idea of the usability of an interface.
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9. Qualitative vs. Quantitative Studies
Use to reliably measure something.
Requires us to know how to measure it.
Examples:
Time to complete a task.
Average number of errors on a task.
Users’ ratings of an interface *:
Ease of use, elegance, performance, robustness, speed,…
* - You could argue that users’ perception of speed, error rates etc is more important than their actual values.
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10. Quantitative Methods
Very often, we want to compare values for two different designs (which is faster?). This requires some statistical methods.
We begin by defining some quantities to measure - variables.
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11. Variable types Independent Variables: the ones you control
Aspects of the interface design
Characteristics of the testers
Discrete: A, B or C
Continuous: Time between clicks for double-click
Dependent variables: the ones you measure
Time to complete tasks
Number of errors
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12. Deciding on Data to Collect
Two types of data
process data
observations of what users are doing & thinking
bottom-line data
summary of what happened (time, errors, success…)
i.e., the dependent variables
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13. Process Data vs. Bottom Line Data
Focus on process data first
gives good overview of where problems are
Bottom-line doesn’t tell you where to fix
just says: “too slow”, “too many errors”, etc.
Hard to get reliable bottom-line results
need many users for statistical significance
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14. The “Thinking Aloud” Method
Need to know what users are thinking, not just what they are doing
Ask users to talk while performing tasks
tell us what they are thinking
tell us what they are trying to do
tell us questions that arise as they work
tell us things they read
Make a recording or take good notes
make sure you can tell what they were doing
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15. Thinking Aloud (cont.) Prompt the user to keep talking
“tell me what you are thinking”
Only help on things you have pre-decided
keep track of anything you do give help on
Recording
use a digital watch/clock
take notes, plus if possible
record audio and video (or even event logs)
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16. Some statistics Variables X & Y A relation (hypothesis) e.g. X > Y
We would often like to know if a relation is true
e.g. X = time taken by novice users
Y = time taken by users with some training
To find out if the relation is true we do experiments to get lots of x’s and y’s (observations)
Suppose avg(x) > avg(y), or that most of the x’s are larger than all of the y’s. What does that prove?
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17. Significance
The significance or p-value of an outcome is the probability that it happens by chance if the relation does not hold.
E.g. p = 0.05 means that there is a 1/20 chance that the observation happens if the hypothesis is false.
So the smaller the p-value, the greater the significance.
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18. Significance
For instance p = means there is a 1/1000 chance that the observation would happen if the hypothesis is false. So the hypothesis is almost surely true.
Significance increases with number of trials.
CAVEAT: You have to make assumptions about the probability distributions to get good p-values.
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19. Normal distributions Many variables have a Normal distribution
At left is the density, right is the cumulative prob.
Normal distributions are completely characterized by their mean and variance (mean squared deviation from the mean).
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20. Normal distributions
The difference between two independent normal variables is also a normal variable, whose variance is the sum of the variances of the distributions.
Asserting that X > Y is the same as (X-Y) > 0, whose probability we can read off from the curve.
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21. Analyzing the Numbers
Example: prove that task 1 is faster on design A than design B.
Suppose the average time for design B is 20% higher than A.
Suppose subjects’ times in the study have a std. dev. which is 30% of their mean time (typical).
How many subjects are needed?
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22. Analyzing the Numbers
Example: prove that task 1 is faster on design A than design B.
Suppose the average time for design B is 20% higher than A.
Suppose subjects’ times in the study have a std. dev. which is 30% of their mean time (typical).
How many subjects are needed?
Need at least 13 subjects for significance p=0.01
Need at least 22 subjects for significance p=0.001
(assumes subjects use both designs)
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23. Analyzing the Numbers (cont.)
i.e. even with strong (20%) difference, need lots of subjects to prove it.
Usability test data is quite variable
4 times as many tests will only narrow range by 2x
breadth of range depends on sqrt of # of test users
This is when online methods become useful
easy to test w/ large numbers of users (e.g., Landay’s NetRaker system)
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24. Lies, damn lies and statistics…
A common mistake (made by famous HCI researchers *)
Increasing n (number of trials) by running each subject several times.
No! the analysis only works when trials are independent.
All the trials for one subject are dependent, because that subject may be faster/slower/less error-prone than others.
* - making this error will not help you become a famous HCI researcher .
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25. Statistics with care: What you can do to get better significance:
Run each subject several times, compute the average for each subject.
Run the analysis as usual on subjects’ average times, with n = number of subjects.
This decreases the per-subject variance, while keeping data independent.
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26. Measuring User Preference
How much users like or dislike the system
can ask them to rate on a scale of 1 to 10
or have them choose among statements
“best UI I’ve ever…”, “better than average”…
hard to be sure what data will mean
novelty of UI, feelings, not realistic setting, etc.
If many give you low ratings -> trouble
Can get some useful data by asking
what they liked, disliked, where they had trouble, best part, worst part, etc. (redundant questions)
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27. B A Using Subjects Between subjects experiment
Two groups of test users
Each group uses only 1 of the systems
Within subjects experiment
One group of test users
Each person uses both systems
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28. Between subjects Two groups of testers, each use 1 system Advantages:
Users only have to use one system (practical).
No learning effects.
Disadvantages:
Per-user performance differences confounded with system differences:
Much harder to get significant results (many more subjects needed).
Harder to even predict how many subjects will be needed (depends on subjects).
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29. Within subjects One group of testers who use both systems Advantages:
Much more significance for a given number of test subjects.
Disadvantages:
Users have to use both systems (two sessions).
Order and learning effects (can be minimized by experiment design).
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30. Example Same experiment as before: Within subjects: Between subjects:
System B is 20% slower than A
Subjects have 30% std. dev. in their times.
Within subjects:
Need 13 subjects for significance p = 0.01
Between subjects:
Typically require 52 subjects for significance p = 0.01.
But depending on the subjects, we may get lower or higher significance.
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31. Experimental Details Order of tasks
choose one simple order (simple -> complex)
unless doing within groups experiment
Training
depends on how real system will be used
What if someone doesn’t finish
assign very large time & large # of errors
Pilot study
helps you fix problems with the study
do 2, first with colleagues, then with real users
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32. Reporting the Results Report what you did & what happened
Images & graphs help people get it!
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33. Summary Use real tasks & representative participants
Be ethical & treat your participants well
Want to know what people are doing & why
i.e., collect process data
Using bottom line data requires more users to get statistically reliable results
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