Taxonomy Testing & Usability

Strategies LLCTaxonomy

March 25, 2006 Copyright 2006 Taxonomy Strategies LLC. All rights reserved.

Taxonomy Testing & Usability

Joseph A. Busch

2Taxonomy Strategies LLC The business of organized information

Agenda

Qualitative methods Quantitative methods


Qualitative taxonomy testing methods

Method Process Who Requires Validation

Walk-thru Show & explain

Taxonomist SME Team

Rough taxonomy

Approach Appropriateness to task

Walk-thru Check conformance to editorial rules

Taxonomist Draft taxonomy

Editorial Rules

Consistent look and feel

Usability Testing

Contextual analysis (card sorting, scenario testing, etc.)

Users Rough taxonomy

Tasks & Answers

Tasks are completed successfully

Time to complete task is reduced

User Satisfaction

Survey Users Rough Taxonomy

UI Mockup Search

prototype

Reaction to taxonomy Reaction to new interface Reaction to search results

Tagging Samples

Tag sample content with taxonomy

Taxonomist Team Indexers

Sample content

Rough taxonomy (or better)

Content ‘fit’ Fills out content inventory Training materials for people &

algorithms Basis for quantitative

methods


Walk-through method—Show & explain

ABC Computers.com

AllBusinessDell EmployeeEducationGaming Enthusiast

HomeInvestorJob SeekerMediaPartnerShopper

First TimeExperiencedAdvanced

Supplier

Audience

AllHome & Home Office

GamingGovernment, Education & Healthcare

Medium & Large Business

Small Business

Line of Business

AllAsia-PacificCanadaDell EMEAJapanLatin America & Caribbean

United States

Region-Country

DesktopsMP3 PlayersMonitorsNetworkingNotebooksPrintersProjectorsServersServicesStorageTelevisionsNon-Dell Brands

Product Family

AwardCase StudyContract & Warranty

DemoMagazineNews & EventProduct Information

ServicesSolutionSpecificationTechnical NoteToolTrainingWhite PaperOther Content Type

Content Type

Business & Finance

Interpersonal Development

IT Professionals Technical Training

IT Professionals Training & Certification

PC ProductivityPersonal Computing Proficiency

Competency Industry

Banking & Finance

Communica-tions

E-BusinessEducationGovernmentHealthcareHospitalityManufacturingPetro-chemocals

Retail / Wholesale

TechnologyTransportationOther Industries

Service

Assessment, Design & Implementation

DeploymentEnterprise Support

Client Support

Managed Lifecycle

Asset Recovery & Recycling

Training


Walk-through method— Editorial rules consistency check

Abbreviations Ampersands Capitalization General…, More…, Other… Languages & character sets Length limits Multiple parents Plural vs. singular form Scope notes Serial comma Sources of terms Spaces Synonyms & acronyms Term order (Alphabetic or …) Term label order (Direct vs.

inverted)…

Rule Name Editorial Rule

Abbreviations Abbreviations, other than colloquial terms and acronyms, shall not be used in term labels.Example: Public InformationNOT: Public Info.

Ampersands The ampersand [&] character shall be used instead of the word ‘and’. Example: Licensing & ComplianceNOT: Licensing and Compliance

Capitalization Title case capitalization shall be used. Example: Customer ServiceNOT: CUSTOMER SERVICENOT: Customer serviceNOT: customer service

General…, More…, Other…

The term labels “General…”, “More…”, and “Other…” shall be used for categories which contain content items that are not further classifiable. Example: “Other Property”

“Other Services”“General Information”“General Audience”

… …


Usability testing method—Task-based card sorting (1)

15 representative questions were selected Perspective of various organizational units Most frequent website searches Most frequently accessed website content Correct answers to the questions were agreed in advance by team.

15 users were tested Did not work for the organization Represented target audiences

Testers were asked “where would you look for …” “under which facet… Topic, Commodity, or Geography?” Then, “… under which category?” Then, “…under which sub-category?” Tester choices were recorded

Testers were asked to “think aloud” Notes were taken on what they said

Pre- and post questions were asked Tester answers were recorded


Usability testing method—Task-based card sorting (2)

3. What is the average farm income level in

your state?

1. Topics2. Commodities3. Geographic Coverage

1. Topics1.1 Agricultural Economy1.2 Agriculture-Related

Policy1.3 Diet, Health & Safety1.4 Farm Financial

Conditions1.5 Farm Practices &

Management1.6 Food & Agricultural

Industries1.7 Food & Nutrition

Assistance1.8 Natural Resources &

Environment1.9 Rural Economy1.10 Trade & International

Markets

1.4 Farm Financial Conditions

1.4.1 Costs of Production1.4.2 Commodity Outlook1.4.3 Farm Financial

Management & Performance

1.4.4 Farm Income1.4.5 Farm Household

Financial Well-being1.4.6 Lenders & Financial

Markets1.4.7 Taxes

Analysis of task-based card sorting (1)

Find-it Tasks User 1 User 2 User 3 User 4 User 5

1. Cotton Cotton Cotton Asia Cotton Cotton

2. Mad cow Cattle Food Safety Cattle Cattle Cattle

3. Farm income Farm Income Farm Income US States Farm Income Farm Income

4. Fast foodFood Consumption

Diet Quality & Nutrition

Food Expenditures



5. WIC WIC Program WIC Program WIC Program WIC Program WIC Program

6. GE Corn Corn Corn Corn Corn Corn

7. Foodborne illnessFoodborne Disease

Foodborne Disease

Consumer Food Safety

Foodborne Disease

Foodborne Disease

8. Food costs Food Prices Market Structure Market AnalysisFood Expenditures

Retailing & Wholesaling

9. Tobacco Tobacco Tobacco Tobacco Tobacco Tobacco

10. Small Farms Farm Structure Farm Structure Farm Structure Farm Structure Farm Structure

11. Traceability Food System Labeling PolicyFood Safety Innovations

Food Safety Policy Food Prices

12. Hunger Food Security Food Security Food Security Food Security Food Security

13. Trade balanceCommodity Trade

Trade & Intl Markets

Commodity Trade Market Analysis

Commodity Trade

14. ConservationsCropping Practices

Conservation Policy

Conservation Policy

Conservation Policy

Conservation Policy

15. Trade restrictions Trade PolicyFood Safety & Trade WTO Market Analysis

Commodity Trade



In 80% of the trials users looked for information under the categories that we expected them to look for it.

Breaking-up topics into facets makes it easier to find information, especially information related to commodities.



Test Questions%

Correct%

Agree

1. Cotton 91% 82%

2. Mad cow 73% 64%

3. Farm income 100% 55%

4. Fast food 91% 73%

5. WIC 100% 100%

6. GE corn 100% 100%

7. Foodborne illness 82% 82%

8. Food costs 55% 27%

9. Tobacco 100% 100%

10. Small farms 91% 91%

11. Traceability 36% 18%

12. Hunger 100% 73%

13. Trade balance 36% 64%

14. Conservation 91% 91%

15. Trade restrictions 55% 36%

Possible change required.

Change required.

Possible error in categorization of this question because 64% thought the answer should be “Commodity Trade.”

On these trials, only 50% looked in the right category, & only 27-36% agreed on the category.

Policy of “Traceability” needs to be clarified. Use quasi-synonyms.


User satisfaction method—Card Sort Questionnaire (1)

Was it easy, medium or difficult to choose the appropriate Topic?

– Easy – Medium– Difficult

Was it easy, medium or difficult to choose the appropriate Commodity?


Was it easy, medium or difficult to choose the appropriate Geographic Coverage?



User satisfaction method—Card Sort Questionnaire (2)

-

0.50

1.00

1.50

2.00

Topic Commodity Geography

Facet

Ea

sy

-

->

Dif

fic

ult

EasierMore Difficult


User interface survey— Which search UI is ‘better’?

Criteria User satisfaction Success completing tasks Confidence in results Fewer dead ends

Methodology Design tasks from specific to

general Time performance Calculate success rates Survey subjective criteria Pay attention to survey

hygiene:– Participant selection– Counterbalancing– T-scores

Source: Yee, Swearingen, Li, & Hearst


User interface survey — Results (1)

Which Interface would you rather use for these tasks?

Google-like Baseline

Faceted Category

Find images of roses 15 16

Find all works from a certain period 2 30

Find pictures by 2 artists in the same media 1 29

…

Overall assessment:Google-like

BaselineFaceted

Category

More useful for your usual tasks 4 28

Easiest to use 8 23

Most flexible 6 24

More likely to result in dead-ends 28 3

Helped you learn more 1 31

Overall preference 2 29

…



User interface survey — Results (2)

6.06.7

4.7 4.6

5.8 5.56.0

4.0

7.26.3

3.5

7.7 7.4 7.8

4.8

7.6

0123456789

Faceted Category

Google-like Baseline



Tagging samples—How many items?

GoalNumber of

Items Criteria

Illustrate metadata schema 1-3 Random (excluding junk)

Develop training documentation

10-20 Show typical & unusual cases

Qualitative test of small vocabulary (<100 categories)

25-50 Random (excluding junk)

Quantitative test of vocabularies *

3-10X number of categories

Use computer-assisted methods when more than 10-20 categories. Pre-existing metadata is the most meaningful.

* Quantitative methods require large amounts of tagged content. This requires specialists, or software, to do tagging. Results may be very different than how “real” users would categorize content.


Tagging samples—Manually tagged metadata sample

Attribute Values

Title Jupiter’s Ring System

URL http://ringmaster.arc.nasa.gov/jupiter/

Description Overview of the Jupiter ring system. Many images, animations and references are included for both the scientist and the public.

Content Types Web Sites; Animations; Images; Reference Sources

Audiences Educators; Students

Organizations Ames Research Center

Missions & Projects Voyager; Galileo; Cassini; Hubble Space Telescope

Locations Jupiter

Business Functions Scientific and Technical Information

Disciplines Planetary and Lunar Science

Time Period 1979-1999


Tagging samples—Spreadsheet for tagging 10’s-100’s of items

1) Clickable URLs for sample content

2) Review small sample and describe

3) Drop-down for tagging (including ‘Other’ entry for the unexpected

4) Flag questions


Rough Bulk Tagging—Facet Demo (1)

Collections: 4 content sources NTRS, SIRTF, Webb, Lessons Learned

Taxonomy Converted MultiTes format into RDF for Seamark

Metadata Converted from existing metadata on web pages, or Created using simple automatic classifier (string matching with

terms & synonyms) 250k items, ~12 metadata fields, 1.5 weeks effort

OOTB Seamark user interface, plus logo


Rough Bulk Tagging— OOTB Facet Demo (2)


Agenda

Qualitative methods Quantitative methods


How evenly does it divide the content?

Documents do not distribute uniformly across categories

Zipf (1/x) distribution is expected behavior

80/20 rule in action (actually 70/20 rule)

Measured v Expected Distribution of Top 10 Content Types in Library of Congress Database

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Congre

sses

Biogra

phy

Period

icals

Map

s

Fiction

Exhib

itions

Juve

nile l

itera

ture

Bibliog

raph

y

Statis

tics

Top 10 Content Types

Nu

mb

er o

f R

eco

rds

Leading candidate for splitting

Leading candidates for merging


How evenly does it divide the content?

Methodology: 115 randomly selected URLs from corporate intranet search index were manually categorized. Inaccessible files and ‘junk’ were removed.

Results: Slightly more uniform than Zipf distribution. Above the curve is better than expected.

Measured v Expected Intranet Content Type Distribution

0

5

10

15

20

25

Peo

ple,

Gro

ups

& P

lace

s

New

s &

Eve

nts

Man

uals

&Le

arni

ngM

ater

ials

Ope

ratio

ns &

Inte

rnal

Com

mun

icat

ions

Mar

ketin

g &

Sal

es

Reg

ulat

ions

,P

olic

ies,

Pro

cedu

res

&T

empl

ates

Pap

ers

&P

rese

ntat

ions

Oth

er &

Unc

lass

ified

Pro

gram

s,P

ropo

sals

, P

lans

& S

ched

ules

Content Type

# D

ocu

men

ts


How intuitive (repeatable) are the categorizations?

Methodology: Closed Card Sort For alpha test of a grocery site 15 Testers put each of 71 best-selling product types into one of

10 pre-defined categories Categories where fewer than 14 of 15 testers put product into

same category were flagged



% of Testers Cumulative % of Products

15/15 54%

14/15 70%

13/15 77%

12/15 83%

11/15 85%

<11/15 100%

With Poly-Hierarchy

69%

83%

93%

100%

100%

100%


How does taxonomy “shape” match that of content?

Background: Hierarchical taxonomies allow

comparison of “fit” between content and taxonomy areas

Methodology: 25,380 resources tagged with

taxonomy of 179 terms. (Avg. of 2 terms per resource)

Counts of terms and documents summed within taxonomy hierarchy

Results: Roughly Zipf distributed (top 20

terms: 79%; top 30 terms: 87%) Mismatches between term% and

document% flagged

Term Group%

Terms%

Docs

Administrators 7.8 15.8

Community Groups 2.8 1.8

Counselors 3.4 1.4

Federal Funds Recipients and Applicants

9.5 34.4

Librarians 2.8 1.1

News Media 0.6 3.1

Other 7.3 2.0

Parents and Families 2.8 6.0

Policymakers 4.5 11.5

Researchers 2.2 3.6

School Support Staff 2.2 0.2

Student Financial Aid Providers

1.7 0.7

Students 27.4 7.0

Teachers 25.1 11.4

Source: Courtesy Keith Stubbs, US. Dept. of Ed.


Pop Quiz

What is the #1 underused source of quantitative information on how to improve your taxonomy?

Query Logs & Click Trails


Query Log & Click Trail Examination—Who are the users & what are they looking for?

Only 30-40% of organizations regularly examine their logs*.

Sophisticated software available, but don’t wait. 80% of value comes from basic reports


Query logs

UltraSeek Reporting Top queries Queries with no results Queries with no click-through Most requested documents Query trend analysis Complete server usage

summary


Click Trail Packages

iWebTrack NetTracker OptimalIQ SiteCatalyst Visitorville WebTrends


Start a “Measure & Improve” mindset

Taxonomy changes do not stand alone Search system improvements Navigation improvements Content improvements Process improvements


Bibliography

K. Yee, K. Swearingen, K. Li, M. Hearst. "Searching and organizing: Faceted metadata for image search and browsing." Proceedings of the Conference on Human Factors in Computing Systems (April 2003) http://bailando.sims.berkeley.edu/papers/flamenco-chi03.pdf

R. Daniel and J. Busch. "Benchmarking Your Search Function: A Maturity Model.” http://www.taxonomystrategies.com/presentations/maturity-2005-05-17%28as-presented%29.ppt

Taxonomy Testing & Usability

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