Strategies LLCTaxonomy Novemver 2, 2006Copyright 2006 Taxonomy Strategies LLC. All rights reserved. Testing Your Taxonomy Ron Daniel, Jr

Strategies LLCTaxonomy

Novemver 2, 2006 Copyright 2006 Taxonomy Strategies LLC. All rights reserved.

Testing Your Taxonomy

Ron Daniel, Jr.

2Taxonomy Strategies LLC The business of organized information

Testing Your Taxonomy

Your taxonomy will not be perfect or complete and will need to be modified based on changing content, user needs, and other practical considerations.

Developing a taxonomy incrementally requires measuring how well it is working in order to plan how to modify it.

In this session, you will learn qualitative and quantitative taxonomy testing methods including: Tagging representative content to see if it works and determining

how much content is good enough for validation. Card-sorting, use-based scenario testing, and focus groups to

determine if the taxonomy makes sense to your target audiences and to provide clues about how to fix it.

Benchmarks and metrics to evaluate usability test results, identify coverage gaps, and provide guidance for changes.


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

AllBusinessEmployeeEducationGaming Enthusiast

HomeInvestorJob SeekerMediaPartnerShopper

First TimeExperiencedAdvanced

Supplier

Audience

AllHome & Home Office

GamingGovernment, Education & Healthcare

Medium & Large Business

Small Business

Line of Business

AllAsia-PacificCanadaEMEAJapanLatin America & Caribbean

United States

Region-Country

DesktopsMP3 PlayersMonitorsNetworkingNotebooksPrintersProjectorsServersServicesStorageTelevisionsOther 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

AbbreviationsAmpersandsCapitalizationGeneral…, More…, Other…Languages & character setsLength limitsMultiple parentsPlural vs. singular formScope notesSerial commaSources of termsSpacesSynonyms & acronymsTerm 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”

… …


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

With Poly-Hierarchy

15/15 54% 69%

14/15 70% 83%

13/15 77% 93%

12/15 83% 100%

11/15 85% 100%

<11/15 100% 100%

“Cocoa Drinks – Powder” is best categorized in both

“Beverages” and “Grocery”.

How to improve? Allow products in multiple categories. (Results are for

minimum size = 4 votes)


Usability Testing Method: Closed Card Sort


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 cardsorting (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

ConsumptionDiet Quality &

NutritionFood

ExpendituresDiet Quality &

NutritionDiet Quality &

Nutrition

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

6. GE Corn Corn Corn Corn Corn Corn

7. Foodborne illnessFoodborne

DiseaseFoodborne

DiseaseConsumer Food

SafetyFoodborne

DiseaseFoodborne

Disease

8. Food costs Food Prices Market Structure Market AnalysisFood

ExpendituresRetailing &

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

TradeTrade & Intl

MarketsCommodity

Trade Market AnalysisCommodity

Trade

14. ConservationsCropping Practices

Conservation Policy

Conservation Policy

Conservation Policy

Conservation Policy

15. Trade restrictions Trade PolicyFood Safety &

Trade WTO Market AnalysisCommodity

Trade


Analysis of task-based cardsorting (2)

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.


Analysis of task-based card sorting (3)

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


Task-Based Card Sorting “Bakeoff”

Goal:Compare two different sets of headings – “Blue” and “Orange”

Method:Scenarios written for 8 general tasks.15 users used one set of headings, then the other, to accomplish the task.Users were surveyed on satisfaction after each task, then again at the end.Be aware of test design and be sure to counterbalance the order in which people see the different schemes!

This is easier with an even number of participants.

Task Both Blue Orange T-Test

Improve Processes with New Technology 6.90 8.80 5.00 0.06

Get Email Remotely 6.20 5.40 7.00 0.26

Look Up Features/Model Information 7.70 8.40 7.00 0.23

Research a Product 6.90 8.20 5.60 0.28

Compare Product Features 6.10 7.00 5.20 0.19

Try out a Trial Copy 8.90 8.40 9.40 0.14

Choose Secure Product 6.90 6.80 7.00 0.93

Choose Software I Already Know 6.10 6.20 6.00 0.95


Strengths and Weaknesses of Task-BasedCard Sorts

A task-based card sort is a test of the navigation headings, without additional context from the viewed pages. Due to the low-fidelity interface, it is easy to create and conduct. As a pure navigation test, it provides concentrated information

about navigation alone. This makes it particularly appropriate for comparing the two navigation

schemes. It provides concentrated information about the wording of headings

and spotlights any confusion they may cause. A tightly focused method to gather this type of information. These appear in the qualitative analysis more than in the quantitative.

Due to the lack of context, it is a difficult test of navigation. Due to the lack of content, users will have limited confidence that

they have reached the right spot. This will be reflected in lower satisfaction scores than for the fully-

implemented navigation.


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

User satisfactionSuccess completing tasksConfidence in resultsFewer dead ends

MethodologyDesign tasks from specific to generalTime performanceCalculate success ratesSurvey subjective criteriaPay attention to survey hygiene:

Participant selectionCounterbalancingT-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)


Quantitative Method: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


Quantitative Method:How evenly does it divide the content? (2)

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


Quantitative Method - 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.


Conclusion

Simple walkthroughs are only the start of how to test a taxonomy.

Tagging modest amounts of content, and usability tests such as task-based card sorts, provide strong information about problems within the taxonomy. Caveat: They may tell you which headings need to be changed,

they won’t tell you what they should be changed to.

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

Strategies LLCTaxonomy

Novemver 2, 2006 Copyright 2006 Taxonomy Strategies LLC. All rights reserved.

Questions?Ron Daniel, [email protected]

http://ww.taxonomystrategies.com


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

Donna Maurer, “Card-Based Classification Evaluation”, Boxes and Arrows, April 7, 2003. http://www.boxesandarrows.com/view/card_based_classification_evaluation