data analysis and interpretation. project part 3 watch for comments on your evaluation plans finish...
TRANSCRIPT
Project part 3
Watch for comments on your evaluation plans Finish your plan
– Finalize questions, tasks– Prepare scripts or tutorials, etc.
Find participants– Friends, neighbors, co-workers
Perform the evaluations– Clearly inform your users what you are doing and why. – If you are audio or video recording, I prefer you use a
consent form.– Pilot at least once – know how long its going to take.
Quantitative and qualitative
Quantitative data – expressed as numbers Qualitative data – difficult to measure sensibly as
numbers, e.g. count number of words to measure dissatisfaction
Quantitative analysis – numerical methods to ascertain size, magnitude, amount
Qualitative analysis – expresses the nature of elements and is represented as themes, patterns, stories
Be careful how you manipulate data and numbers!
Descriptive Statistics
For all variables, get a feel for results:– Total scores, times, ratings, etc.– Minimum, maximum– Mean, median, ranges, etc.
e.g. “Twenty participants completed both sessions (10 males, 10 females; mean age 22.4, range 18-37 years).” e.g. “The median time to complete the task in the mouse-input group was 34.5 s (min=19.2, max=305 s).”
Simple quantitative analysis
Averages – Mean: add up values and divide by number of data points– Median: middle value of data when ranked– Mode: figure that appears most often in the data
Percentages versus numbers Graphical representations give overview of data
Number of errors made
00.5
11.5
22.5
33.5
44.5
1 3 5 7 9 11 13 15 17
UserN
um
be
r o
f e
rro
rs m
ad
e
Internet use
< once a day
once a day
once a week
2 or 3 times a week
once a month
Number of errors made
0
2
4
6
8
10
0 5 10 15 20
User
Nu
mb
er o
f er
rors
mad
e
Subgroup Stats
Look at descriptive stats (means, medians, ranges, etc.) for any subgroups– e.g. “The mean error rate for the mouse-input
group was 3.4%. The mean error rate for the keyboard group was 5.6%.”
– e.g. “The median completion time (in seconds) for the three groups were: novices: 4.4, moderate users: 4.6, and experts: 2.6.”
Simple qualitative analysis
Recurring patterns or themes– Emergent from data
Categorizing data– Categorization scheme may be emergent or pre-specified
Looking for critical incidents– Helps to focus in on key events
Presenting the findings
Only make claims that your data can support
The best way to present your findings depends on the audience,
the purpose, and the data gathering and analysis undertaken
Graphical representations may be appropriate for presentation
Other techniques are:
– Using stories, e.g. to create scenarios based on the data
– Summarizing the findings
Interviews
Raw data:– Audio or video recordings, interviewer notes
Initial processing– Transcribe audio, or expand upon notes
Qualitative processing– Group answers to same question (small # of questions and
people)– Label interesting phrases or words– Put labels on post-its or in software and group labels
Quantitative processing– Gather quantitative responses such as age, etc.– Categorize and count responses (5 liked, 3 disliked, etc.)
Presentation– Summarize responses, tell stories and patterns– Use descriptive quotes
Questionnaire
Raw data:– Tables of questions and numbers or text answers
Quantitative processing– Calculate descriptive stats (means, percentages, etc.) for
each question– Can break into subgroups or use statistics to look for
relationships between items (does age correlate to stronger preferences?)
Qualitative processing– Group answers to same question
Presentation– Present tables & charts of means, percentages, etc.– Explain overall meaning of all the responses
Observation
Raw data:– Audio or video recording, log files, notes
Initial processing:– Transcribe audio, expand notes or take more based on video,
synchronize logs with recordings Quantitative processing
– Record metrics such as errors, times, clicks, etc.– Produce descriptive stats and charts of those metrics
Qualitative processing– Note places where problems occurred, interesting behaviors,
common behaviors Presentation
– Descriptions of common or interesting problems– Videos demonstrating issues, or descriptive quotes– Charts describing quantitative data
Sample Think-aloud categorization
1. Interface problems1. Verbalizations show evidence of dissatisfaction about an aspect
of the interface.2. Verbalizations show evidence of confusion/uncertainty about an
aspect of the interface.3. Verbalizations show evidence of confusion/surprise at the
outcome of an action.4. Verbalizations show evidence that they are having problems
achieving a goal.5. Verbalizations show evidence that the user has made an error.6. The participant I unable to recover from error without external
help from the experimenter.7. The participant makes a suggestion for redesign of the interface.
See pg 380 for more complete example
Experimental Results
How does one know if an experiment’s results mean anything or confirm any beliefs?
Example: 40 people participated, 28 preferred interface 1, 12 preferred interface 2
What do you conclude?
Goal of analysis
Get >95% confidence in significance of result
– that is, null hypothesis disproved Ho: Timecolor = Timeb/w
– OR, there is an influence
– ORR, only 1 in 20 chance that difference occurred due to
random chance
Means Not Always Perfect
Experiment 1
Group 1 Group 2Mean: 7 Mean: 10
1,10,10 3,6,21
Experiment 2
Group 1 Group 2Mean: 7 Mean: 10
6,7,8 8,11,11
Hypothesis Testing
Tests to determine differences– t-test to compare two means– ANOVA (Analysis of Variance) to compare several
means– Need to determine “statistical significance”
“Significance level” (p):– The probability that your null hypothesis was wrong,
simply by chance– p (“alpha” level) is often set at 0.05, or 5% of the time
you’ll get the result you saw, just by chance
Errors
Errors in analysis do occur Main Types:
– Type I/False positive - You conclude there is a difference, when in fact there isn’t
– Type II/False negative - You conclude there is no difference when there is
And then there’s the True Negative…
Drawing Conclusions
Make your conclusions based on the descriptive stats, but back them up with inferential stats
– e.g., “The expert group performed faster than the novice group t(1,34) = 4.6, p > .01.”
Translate the stats into words that regular people can understand
– e.g., “Thus, those who have computer experience will be able to perform better, right from the beginning…”
Tools to support data analysis
Spreadsheet – simple to use, basic graphs– Can even do basic statistical analysis
Statistical packages, e.g. SPSS
Qualitative data analysis tools– Categorization and theme-based analysis, e.g. NVivo,
Atlas.ti
– Quantitative analysis of text-based data
Analysis and Presentation for Part 3
List of problems from HE with severity ratings List of problems found in CW Basic quantitative analysis from your observation Basic qualitative analysis from your observation
– Places where problems occur, general story of what and how people did, etc.
Basic quantitative and qualitative analysis from the questionnaire or interview
– Tables of responses, averages, etc. as appropriate
Interpreting your results
Go through each usability criteria – do results demonstrate support for meeting this criteria or not? How do they?
Discuss any other problems with aspects of the design that your results demonstrate.
Discuss how you would modify the design based on these results.
Monday’s plan
3:30-4:05 Cognitive Walkthrough 1 member of design team (D) will facilitate 4
members of the evaluating team D walks through each task, evaluating team
asks and answers the 4 questions for every action
D makes sure all feedback is written down and takes back to rest of design team
Cognitive Walkthrough pairs
Those Guys – The Purple Parrots JPmBK – CommuniCORP Ideal Concepts – T.H.E. Team Infused Industries - ReallyGoodIdeas
Monday’s plan
4:10-4:45 Heuristic evaluation 1 member of the Design team (D2) demos
the prototype to the evaluating team D2 provides the set of heuristics Evaluating team individually writes down all
problems they see and gives it to D2
Heuristic evaluation teams
The Purple Parrots - JPmBK CommuniCORP – Ideal Concepts T.H.E. Team – Infused Industries ReallyGoodIdeas – Those Guys