Stronger Research Reporting Using Visuals


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Stronger Research Reporting Using Visuals

  1. 1. Stronger Research Reporting Using VisualsApplying Visual Design for Better Research – VCU Workshop5th October, 2011
  2. 2. We live in a time ofunprecedented InformationOverload 2
  3. 3. “ The highest-paid person in the first half of the next century will be the ‘storyteller.’ ” Rolf Jensen, 1996 3
  4. 4. As Story-tellers, we learn.. To write for the reader, not for yourself 4
  5. 5. As Story-tellers, we learn.. To write for the reader, not for yourself A story needs a logical flow 5
  6. 6. As Story-tellers, we learn.. To write for the reader, not for yourself A story needs a logical flow To have a point of view 6
  7. 7. As Story-tellers, we learn.. To write for the reader, not for yourself A story needs a logical flow To have a point of view Only to report data that is vital to telling the story 7
  8. 8. How can visuals help in storytelling? Attention The eyes are drawn like a magnet to images. Less cognitive processing required, especially if Comprehension image is familiar. Complexity Best way to summarise / represent complexity. Can reveal patterns and relationships that would Understanding otherwise be hard to interpret or spot Retention Presence of illustrations significantly improves retention. Aesthetics What’s wrong with wanting it to look good? Timing Graphics reduce time required to explain. Pictures do a far better job of communicating Emotion emotion, and emotion does a far better job of inspiring action. 8
  9. 9. Types of Visuals GraphsIllustrations Data Viz 9
  10. 10. “Best 100 non-fictionbooks of the twentieth century”-
  11. 11. Graphs “When a graph is made, quantitative and categorical information is encoded by a display method. Then theinformation is visually decoded. This visual perception is a vital link. No matter how clever the choice of the information, and no matter how technologically impressive the encoding, a visualization fails if the decoding fails.” (William S. Cleveland, The Elements of Graphing Data, Hobart Press, 1994, p. 1)
  12. 12. To 3D or not to 3D? 5 4 3 Series 1 2 Series 2 1 Series 3 Series 3 0 Series 1
  13. 13. To 3D or not to 3D? 6 4 2 0 Series 1 Series 1
  14. 14. To 3D or not to 3D? 6 4 2 0 Series 1 Series 1
  15. 15. To 3D or not to 3D? 6 4 2 Series 1 0 Category Category Category Category Series 1 1 2 3 4
  16. 16. To 3D or not to 3D? 6 4 Series 1 2 0
  17. 17. To 3D or not to 3D? 6 4 Series 1 2 0
  18. 18. To 3D or not to 3D? 6 4 Series 1 2 0
  19. 19. To 3D or not to 3D? 6 5 4 Series 1 3 Series 2 2 Series 3 1 0 Category 1 Category 2 Category 3 Category 4
  20. 20. Losing Perspective 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  21. 21. Losing Perspective 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  22. 22. Losing Perspective
  23. 23. Areas, Volumes and Magnitudes 0 Category 1 Category 2 Category 3 Category 4
  24. 24. Areas, Volumes and Magnitudes 10.90.8 Ratio of size from Cat 1 to 2 is 1:20.7 BUT ratio or shape area is 1: 0 Category 1 Category 2 Category 3 Category 4
  25. 25. Areas, Volumes and Magnitudes
  26. 26. Areas, Volumes and Magnitudes 1 0.5 0 Category 1 Category 2 Category 3 Category 4Lie factor = size of effect shown in graphic / size of effect in data
  27. 27. Areas, Volumes and Magnitudes
  28. 28. Areas, Volumes and Magnitudes 14 12 10 Series 3 8 Series 2 6 Series 1 4 2 0 Category 1 Category 2 Category 3 Category 4
  29. 29. Areas, Volumes and Magnitudes
  30. 30. Who ate all the Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  31. 31. Who ate all the Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  32. 32. Who ate all the Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  33. 33. Who ate all the Pies?We make angle judgments when we read a pie chart,but we don’t judge angles very well. These judgments are biased; we underestimate acute angles (angles less than 90°) and overestimate obtuse angles (angles greater than 90°).(Naomi Robbins, Creating More Effective Graphs, Wiley, 2005, p. 49)
  34. 34. Who ate all the Pies?
  35. 35. Who ate all the Pies? Sales 1st Qtr 8% 17% 2nd Qtr 3rd Qtr 4th Qtr 22% 58%
  36. 36. Who ate all the Pies? Q1 Q2 8% 8% 13% 17% 17% 22%58% 62% Q3 Q4 9% 8% 10% 6% 23% 26% 60% 58%
  37. 37. Who ate all the Pies? 70% 60% 50% Apples 40% Oranges 30% Bananas 20% Grapefruit 10% 0% Q1 Q2 Q3 Q4Hollands and Spence found that trends are best analyzed with line graphsthan with a series of pie charts. When estimating trends with line graphs,people can use a slope estimation procedure; with pie charts, they mustperform multiple size discriminations between pie slices.Hollands JG, Spence I. Judgments of change and proportion in graphical perception. Hum Factors 1992;34:313-34.
  38. 38. Chart Junk & Data Ink 5 Category 4 2.8 4.5 3 Category 3 1.8 3.5 2 Category 2 4.4 2.5 2 Category 1 2.4 4.3 $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00
  39. 39. Chart Junk & Data Ink Lipkus I M , Hollands J G J Natl Cancer Inst Monogr 1999;1999:149-163, Oxford University PressGillan and Richman found that participants had faster response times and weremore accurate when the data-ink ratio was high than when it was low. Inaddition, integrated tasks (e.g., global comparisons or synthesis judgments)appear to be more affected by the data-ink ratio than are focused tasks (e.g.,selecting the value of a data point).Gillan DJ, Richman EH. Minimalism and the syntax of graphs. Hum Factors 1994;36:619-44
  40. 40. Chart Junk & Data Ink
  41. 41. Recap…Data Integrity – avoid:1. 3 dimensional treatments2. Tricks of perspective3. Lie-factors of area or volume4. Too many pies Data Clarity – avoid: 5. Unnecessary clutter 6. A low data-to-ink ratio
  42. 42. Tufte’s 5 principles of GOOD information design1. Enforce visual comparisons between groups2. Show or suggest causality3. Show multivariate data (more than 2 dimensions)4. Content driven—all about explaining the data5. Completely integrate words, numbers and images
  43. 43. 1. Enforce comparison In other words, we must always ask the question, “compared to what?”.Fortunately, visual comparison is faster and easier than mathematical orconceptual comparison: “visualization made it possible to see the effects of design changes on the pressure distribution of an airplane wing, for example. The same thing could be done with number crunching in theory, but it was a lot more immediate and obvious where things went wrong when the model was actually shown as an image” - Robert Kosara,
  44. 44.  1. Enforce comparisonLondon’s Daily Greenhouse Gas Contribution139 thousand tonnes of carbon dioxide would fill a sphere 521 metres across.To most Londoners, 139 thousand tonnes of carbon dioxide is not a very meaningfulquantity. Illustrating it in the context of London landmarks allows viewers to make itmeaningful for themselves. simplified version
  45. 45. 1. Enforce comparisonNew York Weather for 1980 1980’s weather is compared against ‘normal’ weather averages allowing you toimmediately spot points of difference. simplified version
  46. 46. 2. Suggest CausalityWithout an indication of cause, you can be left wondering whatthe point is. i.e. if you show a trend, it begs the question, why isthis happening?
  47. 47. 2. Suggest Causality
  48. 48. 3. Show Multivariate DataThe world we seek to understand is multivariate. The more variables, the more opportunities we have to see relationships and patterns simplified version
  49. 49. 3. Show Multivariate DataNew York Weather for 19803 Dimensions:-- Temperature - Precipitation- Humidity simplified version
  50. 50. 3. Show Multivariate DataIncrease in oil consumptionoil consumption (Y axis) by year(X axis) and region (stacked area)
  51. 51. 3. Show Multivariate DataIncrease in oil consumptionoil consumption (Y axis) by year(X axis) and region (stacked area)  “Small Multiples” Also called Trellis / Lattice / Grid / Panel Chart
  52. 52. 3. Show Multivariate DataHow BI Customers Use their PlatformsPlatforms, by type of usage, by volume
  53. 53. 3. Show Multivariate DataHow BI Customers Use their PlatformsPlatforms, by type of usage, by volume 
  54. 54. 3. Show Multivariate Data Canadians think it time for a change of government, if they don’t see the Government as being on the right track. And their vote intentions tend to reflect that.  NF Size of the circle is the amount of approval of the premier/PM SK Colour of Circle indicates vote difference • Dark green = 15+ vote lead, • Light green is 5-14 lead,Right Track • White = +/- 5% lead/trail, • Red= 5-14 trail & dark red (no example here) is AB trail by 15 or more MN BC PQ NB ON NS PEI Feds Time for Change
  55. 55. 3. Show Multivariate Data 
  56. 56. 4. Content-DrivenIf there are elements that don’t serve the purpose ofexplaining the data, they are probably chart junk.
  57. 57. 4. Content-DrivenNew York Weather for 1980There is nothing on here that is irrelevant 
  58. 58. 4. Fully integrate words, numbers and imagesAim for the viewer to be able to take in the whole picture in oneglance, so avoid separate, complex legends which need to becontinually referenced to make sense of the data
  59. 59. 4. Fully integrate words, numbers and imagesNew York Weather for 1980Key annotations are present right within the chart  simplified version
  60. 60. 4. Fully integrate words, numbers and imagesDistinct Segments driven by exposure interactions andpsychographic engagementKey annotations are present right within the chart 
  61. 61. Napoleon’s March on Moscow illustrates the principlesEnforce visual Completelycomparisons — integratethe width of the words,tan and black numbers andlines gives you an images—in thisimmediate map, numbercomparison of the sit comfortablysize of with words andNapoleon’s army the only legendat different times is a scale toduring the march give a sense of distanceThe designshould be Showcontent-driven — multivariateNapoleon’s March data —was designed as Napoleon’san anti-war March showsposter…the six: army size,designer was location (in 2passionate about dimensions),the information direction, time,being presented. andThe point of the temperatureposter wasn’t the Show causality — the map shows how thedesign, it was the temperature and river crossings defeated Napoleon.information. simplified version
  62. 62. Quiz: Does this meet all of the criteria? simplified version
  63. 63. Data Visualization “Statistics journals rarely cover graphical methods… Outside of statistics, though, infographics and data visualization are more important. Graphics give a sense of the size of big numbers, dramatize relations between variables, and convey the complexity of data andfunctional relationships… sometimes to more efficiently portray masses of information that their audiences want to see in detail (as with sports scores, stock prices, and poll reports), sometimes to help tell a story (as with annotated maps), and sometimes just for fun:.”- Visualization, Graphics, and Statistics, Andrew Gelman and Antony Unwin, Statistical Computing &Graphics, July, 2010
  64. 64. Data VisualizationInfo-graphics Dynamic Data Visualization Dashboards
  65. 65. Info-graphics Summarize complex information using both decorative as well as data-driven visual elements
  66. 66. Info-graphics
  67. 67. Info-graphics
  68. 68. Dynamic Data Vizualisation Uses motion or other interactive elements to allow the user themselves to explore a dataset for insight
  69. 69. Dynamic Data Vizualisation- Some tools becoming available Many Eyes, (
  70. 70. Dashboards Summarize key statistics into one page or panel of charts
  71. 71. Dashboards
  72. 72. DashboardsUsing Excel ‘Slicers’ for a Dynamic Dash MY AWESOME DASHBOARD Gross Profit Total Sales 90000 80000 Salesperson 5 70000 60000 Salesperson 4 50000 40000 Sum of Total GP Salesperson 3 Sum of Total GP 30000 Sum of Total sales Sum of Total sales 20000 Salesperson 2 10000 0 Salesperson 1 0 50000 100000 150000 200000 Month Product Salesperson Jan-09 Product A Salesperson 1 Feb-09 Product B Salesperson 2 Mar-09 Product C Salesperson 3 Apr-09 Product D Salesperson 4 May-09 Salesperson 5 Jun-09 Jul-09
  73. 73. Illustrations “Ask yourself this: What information are you representing with the written word on a slide that you could replace with a photograph (or other appropriate image or graphic)?.. Images are powerful, efficient and direct. Images can also be used very effectively as mnemonicdevices to make messages more memorable. If people cannot listen andread at the same time, why do most PowerPoint slides contain far morewords that images? … It takes the realization that modern presentations with slides and other multimedia have more in common with cinema (Images and narration) …than they do with written documents.”- Presentation Zen, Garr Reynolds, 2008
  74. 74. Illustrations Use of decorative, non-data driven images to add meaning to your reporting. Source images from good Use images along with quality, legal sources bold words to make your headline points Think like a designer: Simple, bold, colour-matched to your palette, Rule of 3rds For memorability or to emphasise a point pick an But you don’t need to be image that has an one: a tonne of image emotional appeal cute, manipulation tools right in comical, evocative PowerPoint. Don’t be afraid to try!
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  76. 76. That’s all folks! Questions? Contact me: Laura Davies, SVP Laura.davies@visioncritical.com88