In Practice - SLIS 797
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In Practice - SLIS 797

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  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?
  • What data does your library have available (where you work or where you are doing your assessment project), and what is one cause-effect relationship that you hope this data could show?

In Practice - SLIS 797 Presentation Transcript

  • 1. IN PRACTICE: VISUAL AND STATISTICAL THINKING: DISPLAYS OF EVIDENCE FOR MAKING DECISIONS EDWARD R. TUFTE PRESENTATION BY EVE ROSS FOR SLIS 797
  • 2. RELEVANCE Southeastern 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. h t t p s: / / sit e s. go o gle. co m/ sit e/ so ut hea st ernlibra rya sse ssment / p rogra m
  • 3. RELEVANCE Communicating 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” Matthews, J. R. (2007). The evaluation and measurement of library services. W estport, CT: Libraries Unlimited. https://blackboard.sc.edu
  • 4. SUMMARY Cholera Epidemic • Accurate data display • Correct decision Space Shuttle Challenger • Misleading data display • Wrong decision
  • 5. 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
  • 6. TIME SERIES • accurately shows what happened • does not show any causeeffect relationships: ultimately unhelpful
  • 7. CAUSE-EFFECT CAUSE polluted well water EFFECT death from cholera
  • 8. 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
  • 9. TWO PUMPS FEW DEATHS NEARBY
  • 10. BROAD STREET PUMP
  • 11. WEAKNESSES • Distracting level of detail. • What was population density?
  • 12. APPARENT EXCEPTIONS • Brewery, work house • Some deaths far from pump
  • 13. CAUSATION UNCLEAR People left the area. Pump was made unusable. Pollution may have cleared up.
  • 14. CAUSE-EFFECT CAUSE pump handle removed EFFECT cholera epidemic ends
  • 15. CAUSATION UNCLEAR
  • 16. CAUSATION UNCLEAR
  • 17. 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?
  • 18. CHALLENGER LAUNCH
  • 19. 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.
  • 20. CAUSE-EFFECT CAUSE cold temperature EFFECT o-ring damage
  • 21. PRIOR LAUNCH DATA
  • 22. 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)
  • 23. PRIOR LAUNCH DATA
  • 24. 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
  • 25. PRIOR LAUNCH DATA
  • 26. 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)
  • 27. PRIOR LAUNCH DATA (TUFTE)
  • 28. PRIOR LAUNCH DATA (ROSS) 12 10 8 Damage 6 4 2 0 25 35 45 55 Temperature 65 75 85
  • 29. OTHER PRESSURES • Short schedule • Routine event • Public relations • Biggest client
  • 30. 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?
  • 31. STRENGTHS • Highly visual. • Dramatic examples from history. • One positive example to emulate, one negative example to avoid.
  • 32. WEAKNESSES • Tufte is only giving his opinion. • Tufte is not applying his own assessment method. • Impossible to know what would have happened otherwise.
  • 33. TAKEAWAYS • Decision-making: cause-effect. • Cause and effect in same chart. • Order data according to cause. • Pay attention to separating or aggregating data.
  • 34. 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.