in practice - slis 797
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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.
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