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Data analysis and interpretation


Data analysis and interpretation – PowerPoint PPT presentation

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Title: Data analysis and interpretation

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

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
  • If you are audio or video recording, I prefer you
    use a consent form.
  • Pilot at least once know how long its going to

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

Quantitative and qualitative
  • Quantitative data expressed as numbers
  • Qualitative data difficult to measure sensibly
    as numbers, e.g. count number of words to measure
  • Quantitative analysis numerical methods to
    ascertain size, magnitude, amount
  • Qualitative analysis expresses the nature of
    elements and is represented as themes, patterns,
  • 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 (min19.2,
    max305 s).

Simple quantitative analysis
  • Averages
  • Mean add up values and divide by number of data
  • 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

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.

Plot the Data
  • Look for the trends graphically

Other Presentation Methods
Scatter plot
Box plot
Middle 50
Time in secs.
Visualizing log data
Interaction profiles of players in online game
Log of web page activity
Simple qualitative analysis
  • Recurring patterns or themes
  • Emergent from data
  • Categorizing data
  • Categorization scheme may be emergent or
  • 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
  • Other techniques are
  • Using stories, e.g. to create scenarios based on
    the data
  • Summarizing the findings

  • 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
  • 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

  • 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,
  • Explain overall meaning of all the responses

  • 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,
  • Produce descriptive stats and charts of those
  • Qualitative processing
  • Note places where problems occurred, interesting
    behaviors, common behaviors
  • Presentation
  • Descriptions of common or interesting problems
  • Videos demonstrating issues, or descriptive
  • Charts describing quantitative data

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
  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
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?

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

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
Inferential Stats and the Data
Are these really different? What would that mean?
Hypothesis Testing
  • Tests to determine differences
  • t-test to compare two means
  • ANOVA (Analysis of Variance) to compare several
  • 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

  • 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

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

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

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.