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Different Questions, Different Views : Your Best Visualization

Learn about the different ways to visualize data, including comparisons, distributions, and compositions, to effectively analyze and understand patterns and trends. Discover the visual attributes of data and the key aspects to consider when comparing data over time.

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Different Questions, Different Views : Your Best Visualization

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  1. Different Questions, Different Views: Your Best Visualization Lauren Young and Craig W. Abbey AIR Forum May 31, 2017

  2. Inspiration: Abela’s Thought Starter

  3. Data, Information and Knowledge • Dataare raw. They are symbols or isolated and non-interpreted facts. • Informationis data that has been given meaning through interpretation by way of relational connection and pragmatic context. • Knowledgeis information, which has been cognitively processed and integrated into an existing human knowledge structure. Source: S.-O. Tergan and T. Keller (Eds.): Knowledge and Information Visualization, LNCS 3426, 2005. p.3

  4. Thinking Visually • 90% of all information is taken in visually • 65% of all people are visual learners • Most people who are auditory learners (30%; learn by listening) or kinesthetic learners (5%; learn by doing) also use visual cues

  5. Good Visualizations Reveal Information • Guide the viewer to think about patterns in data rather than graphic design, technology, etc. (Tufte) • Clarify data patterns rather than distorting • Encourage comparison of various data elements • Have a clear purpose • Help tell a story

  6. Visual Attributes of Data • The following are perceived to have quantitative values in and of themselves: • Length • Width • 2D position • Size • Intensity • Length and 2D position are perceived with greater precision

  7. A Flowchart Approach to Choosing Visualizations

  8. Comparisons

  9. Visualizing Comparisons Between Items • Key is to be able to quickly distinguish outcomes for each category on a visually salient dimension • Items to be compared may be people, institutions, etc. • Useful approach for data series, as in comparing across a block of survey items • Often use statistics that summarize distribution: count, percentage, average, median

  10. Group Comparison: One Dimension, Few Items “How did we compare to our peers on educational and general expenditures per student FTE last year?” Visualization: Column Chart

  11. Group Comparison: One Dimension, Few Items

  12. Group Comparison: One Dimension, Few Items “How did we compare to our peers in terms of expenditures per student FTE, across different IPEDS expenditure categories, last year?” Visualization: Column Chart

  13. Group Comparison: One Dimension, Few Items

  14. Group Comparison: OneDimension, Many Items “What were the most important and least important college choice factors that our new students identified in our freshman survey?” Visualization: Horizontal Bar Chart

  15. Group Comparison: One Dimension, Many Items

  16. Comparison of Distributions “Where do our six-year graduation rates fall within the statewide distribution, relative to competitors within and outside of our Carnegie class?” Visualization: Multiple Boxplots or Violin Plots

  17. Comparison of Distributions

  18. Comparison of Distributions

  19. Comparison Across TwoCategorical Dimensions “What is the average QPA for students in different decanal units enrolling at different credit hour levels? Visualization: Heat Map

  20. Comparison Across Two Categorical Dimensions

  21. Visualizing Comparisons Across Time Key aspects to look for: • Trends • Rates of change • Variability including seasonality and other cycles • Discontinuity

  22. Comparing Over Few Periods,Few Items “How much did our research expenditures grow over the course of the last 10 years?” Visualization: Column Chart

  23. Comparing Over Few Periods,Few Items

  24. Comparing Over Few Periods, Many Items “How much did our instructional expenditures grow over the last 10 years relative to those of our peers?” Visualization: Line Graph

  25. Comparing Over Few Periods, Many Items

  26. Comparing Over Many Periods “How does the number of enrolled seats in BIO 200 change over the time period leading up to and into the Fall 2015 semester?”

  27. Comparing Over Many Periods

  28. Comparing Over Many PeriodsWith Irregular Observations “How does the pattern of course withdrawal vary between STEM and non-STEM courses across time?” Visualization: Dot Plot

  29. Comparing Over Many PeriodsWith Irregular Observations

  30. Composition

  31. Visualizing Static Composition • Key is to quickly compare size of components; if this isn’t easy you need a different visualization • Readability depends on: • Number of components • Very small components • Large variation in component sizes

  32. Static Composition WithFewer vs. More Categories “What do our undergraduates look like in terms of gender and race/ethnicity?” Visualizations: Pie Chart or Tree Map orColumn Chart

  33. Static Composition WithFewer vs. More Categories

  34. Static Composition WithMore Categories

  35. Static Composition WithMore Categories

  36. Static Composition WithSubcategories “Can you show me the racial and ethnic distribution of our undergraduate population and total up the underrepresented minorities?” Visualization: Stacked 100% Bar of Bar Chart or Stacked 100% Column of Column Chart

  37. Static Composition WithSubcategories

  38. Static Composition WithSubcategories

  39. Visualizing Changes in Composition • Stacked bar or area chart is best • Key decision points in building your visualization: • How many time periods to be shown? • Are absolute or relative differences most important? • Relative  need to show percentages that sum to 100% and bars/areas are of equal size • Absolute  counts or sums are primary even if percentages also shown, bars/areas are unequal

  40. Changes in Composition Over Few Periods “How have the proportions of our institutional revenue that come from various sources changed over the past few years?” Visualization: Stacked 100% Column Chart

  41. Changes in Composition Over Few Periods

  42. Changes in Composition Over Few Periods

  43. Changes in Composition Over Few Periods – Showing Flow “What are the enrollment and success outcomes for our undergraduate Engineering cohort?” Visualization: Stacked 100% Column Chart or Sankey Flow Diagram

  44. Changes in Composition Over Few Periods – Showing Flow

  45. Changes in Composition Over Many Periods “How have the relative numbers of undergraduate, graduate, and professional applicants changed over this year?” Visualization: Stacked Area Chart

  46. Changes in Composition Over Many Periods

  47. Changes in Composition Over Many Periods

  48. Distribution

  49. Visualizing Distributions • Broad description of a numeric variable, shows all values from low to high • Key aspects to be captured: • Spread • Central tendency • Shape of distribution

  50. Distribution of One Variable Over Few Data Points “What’s the grade distributionfor CHEM 101 ?” Visualization: Column Histogram

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