Empirical Foundations for Web Site Usability - PowerPoint PPT Presentation

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Empirical Foundations for Web Site Usability

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Do we see a difference in how the metrics behave in different content categories? ... Content is most important predictor of overall score ... – PowerPoint PPT presentation

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Title: Empirical Foundations for Web Site Usability


1
Empirical Foundations for Web Site Usability

Marti Hearst Melody Ivory Rashmi
Sinha University of California, Berkeley
2
The Usability Gap
3
The Usability Gap
196M new Web sites in the next 5 years Nielsen99
Most sites have inadequate usability Forrester,
Spool, Hurst (users cant find what they want
39-66 of the time)
4
The Problem
  • NON-professionals need to create websites
  • Guidelines are helpful, but
  • Sometimes imprecise
  • Sometimes conflict
  • Usually not empirically founded

5
Ultimate Goal Tools to Help Non-Professional
Designers
  • Examples
  • A grammar checker to assess guideline
    conformance
  • Imperfect
  • Only suggestions not dogma
  • Automatic comparison to highly usable pages/sites
  • Automatic template suggestions

6
A View of Web Site Structure (Newman et al. 00)
  • Information design
  • structure, categories of information
  • Navigation design
  • interaction with information structure
  • Graphic design
  • visual presentation of information and navigation
    (color, typography, etc.)

Courtesy of Mark Newman
7
A View of Web Site Design(Newman et al. 00)
  • Information Architecture
  • includes management and more responsibility for
    content
  • User Interface Design
  • includes testing and evaluation

Courtesy of Mark Newman
8
The Goal
  • Eventually want to assess navigation structure
    and graphic design at the page and site level.
  • Farther down the line information design and
    scent
  • Note we are NOT suggesting we can characterize
  • Aesthetics
  • Subjective preferences

9
The Investigation
  • Can we place web design guidelines onto an
    empirical foundation?
  • Can we build models of good design by looking at
    existing designs?

10
Example Empirical Investigation
  • Is it all about the content?

11
Webby Awards 2000
  • 27 topical categories
  • We used finance, education, community, living,
    health, services
  • 100 judges
  • International Academy of Digital Arts Sciences
  • 3 rounds of judging
  • 2000 sites initially

12
Webby Awards 2000
  • 6 criteria
  • Content
  • Structure navigation
  • Visual design
  • Functionality
  • Interactivity
  • Overall experience
  • Scale 1-10 (highest)
  • Nearly normally distributed across judged sites
  • What are Webby judgements about?

13
Webby Awards 2000
  • The best predictor of the overall score is the
    score for content
  • The worst predictor is visual design

14
So Webbys focus on content!
15
Comparing Two Categories
news
arts
16
Guidelines
  • There are MANY usability guidelines
  • A survey of 21 sets of web guidelines found
    little overlap (Ratner et al. 96)
  • Why?
  • One idea because they are not empirically
    validated
  • So lets figure out what works!

17
Another Empirical Study
Which features distinguish well-designed web
pages?
18
Quantitative Metrics
  • Identified 42 attributes from the literature
  • Roughly characterized
  • Page Composition (e.g., words, links, images)
  • Page Formatting (e.g., fonts, lists, colors)
  • Overall Page Characteristics
  • (e.g., information layout quality, download
    speed)

19
Metrics Used in Study
  • Word Count
  • Body Text Percentage
  • Emphasized Body Text Percentage
  • Text Positioning Count
  • Text Cluster Count
  • Link Count
  • Page Size
  • Graphic Percentage
  • Graphics Count
  • Color Count
  • Font Count

20
Data Collection
  • Collected data for 1898 pages from 163 sites
  • Attempted to collect from 3 levels within each
    site
  • Six Webby categories
  • Health, Living, Community, Education, Finance,
    Services
  • Data constraints
  • At least 30 words
  • No pages with forms
  • Exhibit high self-containment (i.e., no scripts,
    applets, etc.)

21
Method
  • The Webby factor
  • A principle components analysis of the 6
    judgement criteria accounted for 91 of the
    variance
  • Two comparisons
  • Model 1 Top 33 of sites vs. the rest
  • (using the overall Webby score)
  • Model 2 Top 33 of sites vs. bottom 33 (using
    the Webby factor)

22
Questions
  • Can we use the metrics to predict membership in
    top vs. other groups?
  • Do we see a difference in how the metrics behave
    in different content categories?

23
Findings
  • We can accurately classify web pages
  • Linear discriminant analysis
  • Model 1 For top vs. rest
  • 67 correct for overall
  • 73 correct when taking categories into account
  • Model 2 For top vs. bottom
  • 65 correct for overall
  • 80 correct using categories

24
Findings
  • Top 33 vs bottom 33 via Webby factor
  • Linear discriminant analysis
  • Works better when subdivided by category

25
Why does this work?
  • Content is most important predictor of overall
    score
  • BUT there is some predictive power in the visual
    design / navigation criteria
  • Also, it may just be that good design is good
    design all over
  • This result is found in other domains
  • automatic essay grading for one

26
Deeper Analysis
  • Which metrics matter?
  • Linear regression analysis
  • (backward elimination until adjusted R² reduced)
  • All metrics played a role
  • Compared small, medium, and large pages
  • Across the board
  • good pages had significantly smaller graphics
    percentage
  • good pages had less emphasized body text
  • good pages had more colors (on text)

27
Small pages (66 words on average)
  • Good small pages have
  • (according to beta coefficients)
  • slightly more content
  • smaller page sizes
  • fewer graphics
  • more font variations
  • This suggests good small pages
  • Have faster download times
  • corroborated by a download time metric
  • Use different fonts for headers vs the rest of
    the text

28
Medium pages (230 words on average)
  • Good medium pages emphasize less of the body text
  • Good medium pages appear to organize text into
    clusters (e.g., lists and shaded table areas).
  • Good medium pages use colors to distinguish
    headers from body text

29
Large pages (827 words on average)
  • Good large pages have
  • more headers
  • more links
  • are larger but have fewer graphics
  • probably attributable to style sheets

30
Future work
  • Distinguish according to page role
  • Home page vs. content vs. index
  • Better metrics
  • Separate info design, navigation design, graphic
    design
  • Site level as well as page level
  • Compare against results of live user studies

31
Future work
  • Category-based profiles
  • Can use clustering to create profiles of good and
    poor sites for each category
  • These can be used to suggest alternative designs
  • More information CHI 2001 paper

32
More metrics
33
More metrics
34
More metrics
35
Ramifications
  • It is remarkable that such simple metrics predict
    so well
  • Perhaps good design is good overall
  • There may be other factors
  • A foundation for a new methodology
  • Empirical, bottom up
  • But, there is no one path to good design!

36
Related Work
  • Some tools report on easy-to-measure attributes
  • Compare number of links graphics to thresholds
  • Stein (Rating Game), Theng Marsden, Thimbley
    (Gentler)
  • These are not empirically validated
  • Accessibility compliance
  • CAST (Bobby), Scholtz Laskowski
  • Perceptually based heuristics
  • Faraday (Design Advisor)

37
Related Work
  • Web log analysis
  • Traffic-based and time-based analysis
  • Drott, Etgan Cantor, Fuller deGraaff,
    Hochheiser Shneiderman, Sullivan
  • Simulators
  • Webcriteria (Max Site Profiler) makes predictions
    via a pre-defined path
  • Chi, Pirolli, Pitkow generate navigation paths
    from server logs

38
In Summary
  • Automated Usability Assessment should help close
    the Web Usability Gap
  • We can empirically distinguish between highly
    rated web pages and other pages
  • Empirical validation of design guidelines
  • Can build profiles of good vs. poor sites
  • Are validating expert judgements with usability
    assessments via a user study
  • Eventually want to build tools to help end-users
    assess their designs

39
  • More information
  • http//webtango.berkeley.edu
  • http//www.sims.berkeley.edu/hearst
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