This document discusses data visualization and graphing techniques. It covers levels of measurement, principles of graphing, and types of univariate graphs like bar charts, pie charts, histograms, and box plots. It emphasizes graphical integrity and avoiding distortion to clearly communicate the true story of the data.

1. James Neill, 2011 Visualisation of quantitative information

2. Overview Visualisation

3. Approaching data

4. Levels of measurement

5. Principals of graphing

6. Univariate graphs

7. Graphical integrity

8. Visualisation “ Visualization is any technique for creating images, diagrams, or animations to communicate a message.” - Wikipedia

9. Is Pivot a turning point for web exploration? (Gary Flake) ( TED talk - 6 min. )

10. Approaching data

11. Approaching data Entering & screening Exploring, describing, & graphing Hypothesis testing

12. Describing & graphing data THE CHALLENGE: to find a meaningful, accurate way to depict the ‘ true story’ of the data

13. Get your fingers dirty with data

14. Get intimate with your data

15. Clearly report the data's main features

16. Levels of Measurement = Type of Data Stevens (1946)

17. Levels of measurement N ominal / Categorical

18. O rdinal

19. I nterval

20. R atio

21. Discrete vs. continuous Discrete - - - - - - - - - - Continuous ___________

22. Each level has the properties of the preceding levels, plus something more!

23. Categorical / nominal Conveys a category label

24. (Arbitrary) assignment of #s to categories e.g. Gender No useful information, except as labels

25. Ordinal / ranked scale Conveys order , but not distance e.g. in a race, 1st, 2nd, 3rd, etc. or ranking of favourites or preferences

26. Ordinal / ranked example: Ranked importance Rank the following aspects of the university according to what is most important to you (1 = most important through to 5 = least important) __ Quality of the teaching and education __ Quality of the social life __ Quality of the campus __ Quality of the administration __ Quality of the university's reputation

27. Interval scale Conveys order & distance

28. 0 is arbitrary e.g., temperature (degrees C) Usually treat as continuous for > 5 intervals

29. Interval example: 8 point Likert scale

30. Ratio scale Conveys order & distance

31. Continuous, with a meaningful 0 point e.g. height, age, weight, time, number of times an event has occurred Ratio statements can be made e.g. X is twice as old (or high or heavy) as Y

32. Ratio scale: Time

33. Why do levels of measurement matter? Different analytical procedures are used for different levels of data. More powerful statistics can be applied to higher levels

34. Principles of graphing

35. Graphs (Edward Tufte) Visualise data

36. Reveal data Describe

37. Explore

38. Tabulate

39. Decorate Communicate complex ideas with clarity, precision, and efficiency

40. Tufte's graphing guidelines Show the data

41. Avoid distortion

42. Focus on substance rather than method

43. Present many numbers in a small space

44. Make large data sets coherent

45. Tufte's graphing guidelines Maximise the information-to-ink ratio

46. Encourage the eye to make comparisons

47. Reveal data at several levels/layers

48. Closely integrate with statistical and verbal descriptions

49. Graphing steps Identify the purpose of the graph

50. Select which type of graph to use

51. Draw a graph

52. Modify the graph to be clear, non-distorting, and well-labelled.

53. Disseminate the graph (e.g., include it in a report)

54. Software for data visualisation (graphing) Statistical packages e.g., SPSS Spreadsheet packages e.g., MS Excel Word-processors e.g., MS Word – Insert – Object – Micrograph Graph Chart

55. Univariate graphs

56. Univariate graphs Bar graph

57. Pie chart

58. Data plot

59. Error bar

60. Stem & leaf plot

61. Box plot (Box & whisker)

62. Histogram

63. Bar chart (Bar graph) Examine comparative heights of bars

64. X-axis: Collapse if too many categories

65. Y-axis: Count or % or mean?

66. Consider whether to use data labels

67. Use a bar chart instead

68. Hard to read Does not show small differences

69. Rotation / position influences perception Pie chart

70. Data plot & error bar Data plot Error bar

71. Stem & leaf plot Alternative to histogram

72. Use for ordinal, interval and ratio data

73. May look confusing to unfamiliar reader

74. Contains actual data

75. Collapses tails Stem & leaf plot Frequency Stem & Leaf 7.00 1 . & 192.00 1 . 22223333333 541.00 1 . 444444444444444455555555555555 610.00 1 . 6666666666666677777777777777777777 849.00 1 . 88888888888888888888888888899999999999999999999 614.00 2 . 0000000000000000111111111111111111 602.00 2 . 222222222222222233333333333333333 447.00 2 . 4444444444444455555555555 291.00 2 . 66666666677777777 240.00 2 . 88888889999999 167.00 3 . 000001111 146.00 3 . 22223333 153.00 3 . 44445555 118.00 3 . 666777 99.00 3 . 888999 106.00 4 . 000111 54.00 4 . 222 339.00 Extremes (>=43)

76. Box plot (Box & whisker) Useful for interval and ratio data

77. Represents min., max, median, quartiles, & outliers

78. Alternative to histogram

79. Useful for screening

80. Useful for comparing variables

81. Can get messy - too much info

82. Confusing to unfamiliar reader Box plot

83. Histogram For continuous data

84. X-axis needs a happy medium for # of categories

85. Y-axis matters (can exaggerate)

86. Histogram of male & female heights

87. Non-normal distributions

88. Non-normal distributions

89. Histogram of weight

90. Histogram of daily calorie intake

91. Histogram of fertility

92. Example ‘normal’ distribution 1

93. Example ‘normal’ distribution 2

94. Example ‘normal’ distribution 2

95. Effects of skew on measures of central tendency

96. Alternative to histogram

97. Implies continuity e.g., time

98. Can show multiple lines Line graph

99. NOIR Bar chart & pie chart NOI Histogram IR Stem & leaf IR Data plot & box plot IR Error-bar IR Line graph IR Summary: Graphs & levels of measurement

100. Graphical integrity (part of academic integrity)

101. Graphing can be like a bikini. What they reveal is suggestive, but what they conceal is vital. (aka Aaron Levenstein)

102. Graphical integrity "Like good writing, good graphical displays of data communicate ideas with clarity, precision, and efficiency. Like poor writing, bad graphical displays distort or obscure the data, make it harder to understand or compare, or otherwise thwart the communicative effect which the graph should convey." Michael Friendly – Gallery of Data Visualisation

103. Cleveland’s hierarchy

104. Cleveland’s hierarchy: Best to worst Position along a common scale

105. Position along identical, non aligned scales

106. Length

107. Angle-slope

108. Area

109. Volume

110. Color hue - color saturation - density

111. Tufte’s graphical integrity Some lapses intentional, some not

112. Lie Factor = size of effect in graph size of effect in data

113. Misleading uses of area

114. Misleading uses of perspective

115. Leaving out important context

116. Lack of taste and aesthetics

117. If a survey question produces a ‘floor effect’, where will the mean, median and mode lie in relation to one another?

118. Over the last century, the performance of the best baseball hitters has declined. Does this imply that the overall performance of baseball batters has decreased? Review questions

119. Can you complete this table? Level Properties Examples Descriptive Statistics Graphs Nominal /Categorical Ordinal / Rank Interval Ratio Answers: http://wilderdom.com/research/Summary_Levels_Measurement.html

120. Links Presenting Data – Statistics Glossary v1.1 - http://www.cas.lancs.ac.uk/glossary_v1.1/presdata.html

121. A Periodic Table of Visualisation Methods - http://www.visual-literacy.org/periodic_table/periodic_table.html

122. Gallery of Data Visualization - http://www.math.yorku.ca/SCS/Gallery/

123. Univariate Data Analysis – The Best & Worst of Statistical Graphs - http://www.csulb.edu/~msaintg/ppa696/696uni.htm

124. Pitfalls of Data Analysis – http://www.vims.edu/~david/pitfalls/pitfalls.htm

125. Statistics for the Life Sciences – http://www.math.sfu.ca/~cschwarz/Stat-301/Handouts/Handouts.html

126. References Cleveland, W. S. (1985). The elements of graphing data . Monterey, CA: Wadsworth.

127. Jones, G. E. (2006). How to lie with charts . Santa Monica, CA: LaPuerta.

128. Tufte, E. (1983). The visual display of quantitative information . Cheshire, CT: Graphics Press.

129. Open Office Impress This presentation was made using Open Office Impress.

130. Free and open source software.

131. http://www.openoffice.org/product/impress.html