IN PRACTICE:

VISUAL AND STATISTICAL THINKING:DISPLAYS OF EVIDENCE FOR MAKING DECISIONSEDWARD R. TUFTE

PRESENTATION BY

EVE ROSS FOR SLIS 797

RELEVANCESoutheastern Library Assessment Conference

Pivot Tables and Data Visualization for the Rookie Analyst

Tricia Clayton, Georgia State University

This session is intended for those who are new to quantitative data analysis and presentation....  The session will also introduce data visualization best practices that will ensure effective communication of assessment results to administrators and stakeholders. 

https: / /s i tes.google.com/si te/southeasternl ibraryassessment/program

RELEVANCECommunicating Assessment Results (Lecture Week 9)

• Verbal/visual communication

• Integrity

Matthews, Chapter 16

• Measuring library effectiveness: are we doing the right things?

Assessment Project Summary

• “virtual presentation outlining your assessment project”Mat thews, J . R. (2007) . The eva luat ion and measurement

o f l ib rary serv ices . Westpor t , CT: L ibrar ies Unl imi ted.

h t tps: / /b lackboard.sc .edu

SUMMARY

Cholera Epidemic• Accurate data display• Correct decision

Space Shuttle Challenger• Misleading data display• Wrong decision

CHOLERA EPIDEMIC

• Seeking cause-effect relationship• competing theories about how cholera spread• unknown what action would stop the outbreak

• Data available: • who died of cholera• where they died• date of death• sometimes surviving family available for interview

TIME SERIES• accurately shows what

happened• does not show any cause-

effect relationships: ultimately unhelpful

CAUSE-EFFECT

CAUSE

polluted well water

EFFECT

death from cholera

METHOD

• Water pump hypothesis• Get list of deaths (who, where, when)• Mark each death on a map • Mark each water pump on the map • Go door-to-door interviewing survivors near

suspected polluted well about each deceased’s use of water from that well.

• Go door-to-door interviewing apparent exceptions to hypothesis

TWO PUMPSFEW DEATHS NEARBY

BROAD STREET PUMP

WEAKNESSES

• Distracting level of detail.

• What was population density?

APPARENT EXCEPTIONS

• Brewery, work house

• Some deaths far from pump

CAUSATION UNCLEAR

People left the area.

Pump was made unusable.

Pollution may have cleared up.

CAUSE-EFFECT

CAUSE

pump handle removed

EFFECT

cholera epidemic ends

CAUSATION UNCLEAR

CAUSATION UNCLEAR

DISCUSSION BOARD 1

• Is there a cause-effect relationship you would like to be able to show for your library (demonstrating effectiveness of library services)?

• What data would you need, in order to show the cause-effect at work?

• How would you display the data in order to reveal that cause-effect relationship accurately?

CHALLENGER LAUNCH

WILL COLD WEATHER DAMAGE O-RINGS?

• Thiokol sent charts and recommended no launch.

• NASA found fault with charts.

• Thiokol agreed charts were inconclusive, allowed launch.

• Cold temperature damaged the o-rings, which led to explosion.

CAUSE-EFFECT

CAUSE

cold temperature

EFFECT

o-ring damage

PRIOR LAUNCH DATA

DATA DISPLAY PROBLEMS

• shows so many separate kinds of damage: hard to see what had the worst damage overall

• no temperature data: data on effects (damage) is unhelpful without data about potential cause (temperature)

PRIOR LAUNCH DATA

DATA DISPLAY PROBLEMS

• omits relevant data (22 shuttles)• includes irrelevant data (4 test rockets)• sample size of 2 relevant shuttle

launches is too small: NASA was right to call this inconclusive

PRIOR LAUNCH DATA

DATA DISPLAY PROBLEMS

• cute rockets are distracting• temperature numbers are sideways:

possible cause should be clearer• should be in temperature order to show

possible cause-effect relationship, not time order (time is not a cause or effect here)

PRIOR LAUNCH DATA (TUFTE)

PRIOR LAUNCH DATA (ROSS)

25 35 45 55 65 75 850

2

4

6

8

10

12

Temperature

Damage

OTHER PRESSURES

• Short schedule

• Routine event

• Public relations

• Biggest client

DISCUSSION BOARD 2

• What factors can cause a library’s assessment data to get distorted when presented to stakeholders?

• What steps could librarians take to prevent that distortion?

STRENGTHS

• Highly visual.

• Dramatic examples from history.

• One positive example to emulate, one negative example to avoid.

WEAKNESSES

• Tufte is only giving his opinion.

• Tufte is not applying his own assessment method.

• Impossible to know what would have happened otherwise.

TAKEAWAYS

• Decision-making: cause-effect.

• Cause and effect in same chart.

• Order data according to cause.

• Pay attention to separating or aggregating data.

REFERENCES

All tables and images taken from:

Tufte, E. R. (1997). Visual and statistical thinking: Displays of evidence for making decisions. Cheshire, CT: Graphics Press.