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Data analysis and interpretation

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Data analysis and interpretation – PowerPoint PPT presentation

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Title: Data analysis and interpretation


1
Data analysis and interpretation
2
Agenda
  • Part 2 comments
  • Average score 87
  • Part 3 due in 2 weeks
  • Data analysis

3
Project part 3
  • Please read the 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.

4
Part 3 write up
  • State exactly what you did (task list, how many,
    questionnaires etc.)
  • Summarize data collected
  • Summarize usability conclusions based on your
    data
  • Discuss implications for the prototype based on
    those conclusions

5
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!

6
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 (min19.2,
    max305 s).

7
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

8
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.

9
Plot the Data
  • Look for the trends graphically

10
Other Presentation Methods
Scatter plot
Box plot
Middle 50
Age
low
high
Mean
0
20
Time in secs.
11
Visualizing log data
Interaction profiles of players in online game
Log of web page activity
12
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

13
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

14
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

15
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

16
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

17
Sample Think-aloud categorization
  1. Interface problems
  2. Verbalizations show evidence of dissatisfaction
    about an aspect of the interface.
  3. Verbalizations show evidence of
    confusion/uncertainty about an aspect of the
    interface.
  4. Verbalizations show evidence of
    confusion/surprise at the outcome of an action.
  5. Verbalizations show evidence that they are having
    problems achieving a goal.
  6. Verbalizations show evidence that the user has
    made an error.
  7. The participant I unable to recover from error
    without external help from the experimenter.
  8. The participant makes a suggestion for redesign
    of the interface.

See pg 380 for more complete example
18
Experimental Results
  • How does one know if an experiments results mean
    anything or confirm any beliefs?
  • Example 40 people participated, 28 preferred
    interface 1, 12 preferred interface 2
  • What do you conclude?

19
Goal of analysis
  • Get gt95 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

20
Means Not Always Perfect
Experiment 1 Group 1 Group 2 Mean 7
Mean 10 1,10,10 3,6,21
Experiment 2 Group 1 Group 2 Mean 7
Mean 10 6,7,8 8,11,11
21
Inferential Stats and the Data
Are these really different? What would that mean?
22
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 youll get the result you saw, just by
    chance

23
Errors
  • Errors in analysis do occur
  • Main Types
  • Type I/False positive - You conclude there is a
    difference, when in fact there isnt
  • Type II/False negative - You conclude there is no
    difference when there is
  • And then theres the True Negative

24
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 gt .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

25
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. N6
  • Quantitative analysis of text-based data

26
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

27
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.
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