data display techniques
DESCRIPTION
Data Display Techniques. Christine R. Curran, PhD, RN, CNA October, 2001. Data Versus Information. How does one determine which display format to use: Text, Table, Graph, Other…? How does display content / “ink” affect the amount of information obtained? rounding of numbers - PowerPoint PPT PresentationTRANSCRIPT
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Data Display TechniquesChristine R. Curran, PhD, RN, CNAOctober, 2001
(c) Chris Curran, 2001
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Data Versus InformationHow does one determine which display format to use: Text, Table, Graph, Other?How does display content / ink affect the amount of information obtained?rounding of numbers Labels: when and where use of white spaceHow does color affect our ability to see information?
(c) Chris Curran, 2001
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Human Cognitive ProcessesHumans want to organize dataThe human mind operates by associationHumans process data through data reduction strategiesChunking of dataPattern recognition & exceptions to patterns are used to make judgmentsAnalogy & metaphor are often used in learning & recall of information
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Data Displays Should FacilitatePerception of salient featuresComprehension of informationRecall of the information
(c) Chris Curran, 2001
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Data Versus InformationMethods used to glean information from the volumes of data available to us:tools (calculators, computers)decision support systemsdata presentation
(c) Chris Curran, 2001
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The data and the type of task drive the choice of display format How to Choose a Display Format
(c) Chris Curran, 2001
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Types of Data DisplaysWordsHeadingsTextNumbersDigitalNumericTableAnalogPictureGraphIconVideo
(c) Chris Curran, 2001
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WordsAvoid all capital lettersUse labels or symbols rather than a keyUse Serif font for textUse San-serif font for headings
(c) Chris Curran, 2001
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Text: SamplesTITLE Text should be displayed in Serif font. One should avoid all capital letters.
Title Text should be displayed in Serif font. One should avoid all capital letters.
(c) Chris Curran, 2001
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Properties of Numerical Data Displays Digitaltask: symbolicdata: discrete, quantitativefocus:specificprocess: analysisdisplay: table
Analogtask: spatialdata: continuous, qualitativefocus:holisticprocess: perceptiondisplay: graph, icon
(c) Chris Curran, 2001
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Principles of Numerical Data DisplaysArrange data to convey meaningproximity of datause of white spacenavigationMake patterns and exceptions within the data obvious at a glance (seeing the data)roundinglabeling & spacingdisplay format
(c) Chris Curran, 2001
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Digital Display: TablesUse in small data sets (20 numbers to be displayed or less)Used to display numbers
(c) Chris Curran, 2001
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Rules for Table Displays Ehrenberg, 1977Round to 2 significant or effective digitseliminate leading 0 trailing 0 does not matterPut figures to be compared in columns rather than in rowsAdd row & column averages (make the main effects explicit)Order rows & columns by sizeShow larger numbers above smaller numbers
(c) Chris Curran, 2001
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Rules for Table Displays Ehrenberg, 1977Spacing & layoutWhite space is your friendUse white space to signal the chunks of dataSingle spacing guides the eye down the columnUse gaps (white space) between groups (columns or rows) to guide the eye across the data & to cluster dataData meant to be compared should be close together
(c) Chris Curran, 2001
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Data RoundingAnyone who cannot learn to cope with rounding errors will probably not get much out of statistical data Ehrenberg, 1977, pg. 282
(c) Chris Curran, 2001
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PrincipleThe Data should drive the order of the presentation. Displays should not be configured by the structure of the data collection methodology or analysis.
(c) Chris Curran, 2001
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Table: Example
(c) Chris Curran, 2001
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Table: Revised Example
(c) Chris Curran, 2001
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Correlation Matrix: Example
(c) Chris Curran, 2001
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Correlation Matrix: Example
(c) Chris Curran, 2001
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Correlation Matrix: Revised Example
(c) Chris Curran, 2001
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Graphical Data Display: A Form of Decision SupportGoalsfind relevant data in a dynamic environmentvisualize the semantics of the domainreconceptualize the nature of the problem
(Bennett, Toms & Woods, 1993)
(c) Chris Curran, 2001
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The Power of a GraphEnables one to take in quantitative information in a qualitative way, organize it, and see patterns and structure not readily revealed by other means.
(c) Chris Curran, 2001
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Graphical Perception The process of visual decoding of quantitative and categorical data from a graph. Cleveland, 1984
(c) Chris Curran, 2001
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Analog Display: GraphsUsed to display large datasetsTypes of Graphs:
Universal - Literal Continuum
(c) Chris Curran, 2001
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Universal Graph: Example
(c) Chris Curran, 2001
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Literal Graph
(c) Chris Curran, 2001
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Graphical Design Concepts & PrinciplesSemantic Mapping (Roscoe, 1968; Kosslyn, 1989)Configural Displays (Garner, 1970)Chunking (Newell & Simon, 1973)Theory of Graph Comprehension (Pinker, 1981)8 Visual Variables (Bertin, 1981)Emergent Features (Pomerantz, 1981)Data-Ink Ratio & Small Multiple (Tufte, 1983,1990, 1997)Elementary Perceptual Tasks (Cleveland & McGill, 1984)Proximity Compatibility (Wickens, 1986)Metaphor Graphics (Cole, 1988)Cognitive Fit (Vessey, 1991)
(c) Chris Curran, 2001
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Design Principles for Computer Displays (Cole, 1994)
Design for the analog mind and both hemispheresDesign for correct encoding of information (represent the users model)Provide a clear context
(c) Chris Curran, 2001
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Graphic Design
(c) Chris Curran, 2001
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Visual Decoding of Graphs Requires Pattern PerceptionPattern perception requires:detectionvisual grouping of a patternestimation
(c) Chris Curran, 2001
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Elementary Perceptual Tasks(ordered from most to least accurate)Position along a common scalePositions along nonaligned scalesLength, Direction, AngleAreaVolume, CurvatureShading, Color Saturation Cleveland & McGill, 1984
(c) Chris Curran, 2001
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Position Along a Common Scale
(c) Chris Curran, 2001
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Position Along Non-Aligned Scales
(c) Chris Curran, 2001
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Length
(c) Chris Curran, 2001
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Direction
(c) Chris Curran, 2001
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Angle
(c) Chris Curran, 2001
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Area
(c) Chris Curran, 2001
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Volume
(c) Chris Curran, 2001
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Curvature
(c) Chris Curran, 2001
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Shading
(c) Chris Curran, 2001
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Color Saturation
(c) Chris Curran, 2001
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Elementary Perceptual TasksCleveland & McGill, 1984
(c) Chris Curran, 2001
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Common Graphs by Elementary Perceptual Task
(c) Chris Curran, 2001
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Recommendations: Based on Graphical PerceptionParts of a Wholedot chartgrouped dot chartbar charts (instead of divided bars or pie charts)Framed Rectangle Charts(instead of Shaded Statistical Maps
Cleveland & McGill, 1984
(c) Chris Curran, 2001
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Dot Chart
(c) Chris Curran, 2001
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Grouped Dot Chart
(c) Chris Curran, 2001
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Bar Charts
(c) Chris Curran, 2001
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Grouped Bar Chart
(c) Chris Curran, 2001
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Divided Bar Chart
(c) Chris Curran, 2001
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Pie Chart
(c) Chris Curran, 2001
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Framed Rectangle
(c) Chris Curran, 2001
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Research Findings: Graphical PerceptionPerception of ChangeLine GraphsGrouped Bar GraphsPerception of ProportionPie ChartsDivided Bar Graphs(differs from Cleveland & McGill)
Hollands & Spence, 1992
(c) Chris Curran, 2001
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Cognitive Fit Vessey, 1991
(c) Chris Curran, 2001
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Proximity Compatibility PrincipleTo the extent that multiple aspects of data or information must be mentally integrated, they should be physically integrated or proximate in the display. Wickens, 1986
(c) Chris Curran, 2001
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Emergent FeaturesA property of the configuration of multiple dimensions of an object that does not exist when the dimensions are specified independently. Pomerantz, 1981
(c) Chris Curran, 2001
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Innovative New DesignsMetaphor Graphics
(c) Chris Curran, 2001
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Clinical Data DisplayCole, 1988
(c) Chris Curran, 2001
Bugs
&A
Page &P
Cole DB Single
&A
Page &P
Patient Died
Each Patient = one icon
Metastases
Thick deep extremity primary tumor
Thin deep axial primary tumor
Lymph nodes removed
Cole DB Screen
High
Integrality
Low
LowHigh
Meaningfulness
Male
Female
&A
Page &P
The higher on the vertical scale and the more to the right, the better the graph Cole, 1995
Tufte EMR Single
-1yradmission datetoday's date
result type, medication, etc.most recent value
critically elevated+Note: initials, credentials: date
elevated+Note: initials, credentials: date
normal rangeNote: initials, credentials: date
reduced-
critically reduced-
&A
Page &P
LastName, FirstName, MI Admission Date Today's Date Patient Location
Problem List: Diagnosis 1, diagnosis 2, Diagnosis 3, etc.
More than 1 year prior to admission
One year prior to admission
First week of admission
Today's value
Tufte EMR Screen
&A
Page &P
Vessey
&A
Page &P
Problem Representation
Problem Solving Task
Mental Representation
Problem Solution
Table Demo
[From Nursing Research (1996), 45(6), pg.354]
Revised Table
Table 1.Summary Statistics of the Independent VariablesTable 1.Summary Statistics of the Independent Variables
VariableMSDMinimumMaximumVariableMSDMinimumMaximum
Total Sample
Weight179.2840.14104300Total Sample
Systolic Blood Pressure130.6816.3993182Cholesterol Level19430107339
Diastolic Blood Pressure77.1111.0152111Weight17940104300
Cholesterol Level194.4430.11107339Systolic Blood Pressure1311693182
Coping Score0.230.45-0.781.41Diastolic Blood Pressure771152111
Type ACoping Score0.20.5-0.81.4
Weight188.5139.16112298
Systolic Blood Pressure138.1616.98107182Type A
Diastolic Blood Pressure79.8311.0454111Cholesterol Level20644112339
Cholesterol Level205.5443.74112339Weight18939112298Revised Table
Coping Score-0.130.31-0.780.97Systolic Blood Pressure13817107182Table 1.Summary Statistics of the Independent Variables
Type BDiastolic Blood Pressure801154111VariableType AType BTotal`
Weight170.5739.22104300Coping Score-0.10.3-0.81.0Mean
Systolic Blood Pressure123.6112.1793155
Diastolic Blood Pressure74.5310.3752102Type BCholesterol Level206184194
Cholesterol Level183.9638.46107316Cholesterol Level18438107316Weight189171179
Coping Score0.570.260.101.41Weight17139104300Systolic Blood Pressure138124131
Systolic Blood Pressure1241293155Diastolic Blood Pressure807577
Diastolic Blood Pressure751052102Coping Score-0.10.60.2
Coping Score0.60.30.11.4Range
Cholesterol Level112-339107-316107-339
Weight112-298104-300104-300
Systolic Blood Pressure107-18293-15593-182
Diastolic Blood Pressure54-11152-10252-111
Coping Score-0.8 to 1.00.1 to 1.4-0.8 to 1.4
&A
Page &P
Correlation Demo
Correlations
(From SPSS)Trial 1Trail 2Trial 3Trial 4
Trial 11.0000.4880.2460.223
Trial 20.4881.0000.8120.803
Trial 30.2460.8121.0000.785
Trial 40.2230.8030.7851.000
**Correlation is significant at the 0.01 level (2-tailed).
RevisedCorrelations
(Option #1)Trial 2Trial 3Trial 4Trial 1
Trial 20.80.80.5
Trial 30.80.80.2
Trial 40.80.80.2
Trial 10.50.20.2
(Option #2)RevisedCorrelations
Trial 2Trial 3Trial 4Trial 1
Trial 20.810.800.49
Trial 30.780.25
Trial 40.22
Trial 1
&A
Page &P
Universal Graph
&A
Page &P
Universal Graph
1532.13279.21702880
2084169.71542520
2018.4205.951622700
1999.5255.48135.53240
2238.0594942700
2217.25248.42582700
2414.05263.288.252895
2015.2518760.254860
82582.4524900
132.3110.99495040
137199.9128900
&A
Page &P
Graph Types
Case1Case2Case3Case4Case5Case6Case5Case2Case1Case4Case3Case6
A354215244910494235241510
B21142341322
C215026867
D14338182644
E4327153313
F93519413125
A_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _
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010203040
Group 1
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B_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
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D_ _ _ _ _ _
Group 2
A_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
D_ _ _ _ _
Group3
A_ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
D_ _ _ _ _ _ _ _ _ _ _ _ _ _
010203040
&A
Page &P
Graph Types
0
0
0
0
0
&A
Page &P
Case1
Colors
00000
00000
00000
&A
Page &P
A
B
C
D
E
Patient Metaphor
000
000
000
000
&A
Page &P
A
B
C
Continuum
0
0
0
0
0
&A
Page &P
Sheet14
0
0
0
0
0
0
&A
Page &P
A
Sheet15
0
0
0
&A
Page &P
Sheet16
Expressive Values of Color
ColorSymbolismAltered ColorSymbolism
YellowUnderstandingMuted Yellowuntruth
Knowledgedistrust
"Heavenly"betrayal
RedPassion
Burning
Blood
BlueNervous SystemShadowy Bluefear
Immortalitysuperstition
Faithgrief
GreenContentmentdecay
Fruitfulness
Hope
VioletPiety
Regal
&A
Page &P
Unusual
Information
&A
Page &P
Head
Xray and Imaging
Respiratory
Routine Blood Work
Cardiac
Gastrointestinal
I&O
Old Lab Work
UniversalLiteral
Line GraphMetaphorVideo
Bar GraphGraphicsPicture
&A
Page &P
&A
Page &P
&A
Page &P
&A
Page &P
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Metaphor Graphics: Database Display (Cole, 1988)MaleFemale
(c) Chris Curran, 2001
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Clinical Data Display
(c) Chris Curran, 2001
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Metaphor Icon Graph: Example
(c) Chris Curran, 2001
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Metaphor Icon Graph: Questionable Example
(c) Chris Curran, 2001
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Clinical Data DisplayPowsner, S. & Tufte,E.R., 1994
(c) Chris Curran, 2001
Schneiderman Model
DataVisualForm
Data TransformationsVisual MappingsView Transformations
Human Interaction
&A
Page &P
Raw Data
Data Tables
Visual Structures
Views
Task
Bugs
&A
Page &P
Cole DB Single
&A
Page &P
Patient Died
Each Patient = one icon
Metastases
Thick deep extremity primary tumor
Thin deep axial primary tumor
Lymph nodes removed
Cole DB Screen
High
Integrality
Low
LowHigh
Meaningfulness
Male
Female
&A
Page &P
The higher on the vertical scale and the more to the right, the better the graph Cole, 1995
Tufte EMR Single
-1yradmission datetoday's date
result type, medication, etc.most recent value
critically elevated+Note: initials, credentials: date
elevated+Note: initials, credentials: date
normal rangeNote: initials, credentials: date
reduced-
critically reduced-
&A
Page &P
LastName, FirstName, MI Admission Date Today's Date Patient Location
Problem List: Diagnosis 1, diagnosis 2, Diagnosis 3, etc.
More than 1 year prior to admission
One year prior to admission
First week of admission
Today's value
Tufte EMR Screen
Public, John Q.admitted 6/4/996/10/996th Intermediate Care
Right lower lobe pneumonia, new onset diabetes, history of manic depressive disorder
WBC 11100c/ulGlucose 237 mg/dlMood 0
+++Discharge RS MD 1200 6/10/99
+++
[[[Note 2
---
---Note 3
T 98.8* FReg Insulin 3unitsLi .56 mmol/l
+++Note 4
+++
[[[
---
---
R 18 resp/minCa 8.7 md/dlCl 100 mmol/l
+++
+++
[[[
---
---
Cefuroxime 1.5gNa 136 mmol/lCO2 23.7 mmol/l
+++
+++
[[[
---
---
Clindamycin900mcg
+
+
[
-
-
&A
Page &P
Vessey
&A
Page &P
Problem Representation
Problem Solving Task
Mental Representation
Problem Solution
Table Demo
[From Nursing Research (1996), 45(6), pg.354]
Revised Table
Table 1.Summary Statistics of the Independent VariablesTable 1.Summary Statistics of the Independent Variables
VariableMSDMinimumMaximumVariableMSDMinimumMaximum
Total Sample
Weight179.2840.14104300Total Sample
Systolic Blood Pressure130.6816.3993182Cholesterol Level19430107339
Diastolic Blood Pressure77.1111.0152111Weight17940104300
Cholesterol Level194.4430.11107339Systolic Blood Pressure1311693182
Coping Score0.230.45-0.781.41Diastolic Blood Pressure771152111
Type ACoping Score0.20.5-0.81.4
Weight188.5139.16112298
Systolic Blood Pressure138.1616.98107182Type A
Diastolic Blood Pressure79.8311.0454111Cholesterol Level20644112339
Cholesterol Level205.5443.74112339Weight18939112298Revised Table
Coping Score-0.130.31-0.780.97Systolic Blood Pressure13817107182Table 1.Summary Statistics of the Independent Variables
Type BDiastolic Blood Pressure801154111VariableType AType BTotal`
Weight170.5739.22104300Coping Score-0.10.3-0.81.0Mean
Systolic Blood Pressure123.6112.1793155
Diastolic Blood Pressure74.5310.3752102Type BCholesterol Level206184194
Cholesterol Level183.9638.46107316Cholesterol Level18438107316Weight189171179
Coping Score0.570.260.101.41Weight17139104300Systolic Blood Pressure138124131
Systolic Blood Pressure1241293155Diastolic Blood Pressure807577
Diastolic Blood Pressure751052102Coping Score-0.10.60.2
Coping Score0.60.30.11.4Range
Cholesterol Level112-339107-316107-339
Weight112-298104-300104-300
Systolic Blood Pressure107-18293-15593-182
Diastolic Blood Pressure54-11152-10252-111
Coping Score-0.8 to 1.00.1 to 1.4-0.8 to 1.4
&A
Page &P
Correlation Demo
Correlations
(From SPSS)Trial 1Trail 2Trial 3Trial 4
Trial 11.0000.4880.2460.223
Trial 20.4881.0000.8120.803
Trial 30.2460.8121.0000.785
Trial 40.2230.8030.7851.000
**Correlation is significant at the 0.01 level (2-tailed).
RevisedCorrelations
(Option #1)Trial 2Trial 3Trial 4Trial 1
Trial 20.80.80.5
Trial 30.80.80.2
Trial 40.80.80.2
Trial 10.50.20.2
(Option #2)RevisedCorrelations
Trial 2Trial 3Trial 4Trial 1
Trial 20.810.800.49
Trial 30.780.25
Trial 40.22
Trial 1
&A
Page &P
Universal Graph
&A
Page &P
Universal Graph
1532.13279.21702880
2084169.71542520
2018.4205.951622700
1999.5255.48135.53240
2238.0594942700
2217.25248.42582700
2414.05263.288.252895
2015.2518760.254860
82582.4524900
132.3110.99495040
137199.9128900
&A
Page &P
Graph Types
Case1Case2Case3Case4Case5Case6Case5Case2Case1Case4Case3Case6
A354215244910494235241510
B21142341322
C215026867
D14338182644
E4327153313
F93519413125
A_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _
D_ _ _ _ _ _
E_ _ _ _ _ _ _ _ _ _ _ _
F_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
G_ _ _ _ _ _ _ _ _ _ _ _ _ _
H_ _ _ _ _
010203040
Group 1
A_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _
D_ _ _ _ _ _
Group 2
A_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
D_ _ _ _ _
Group3
A_ _ _ _ _ _
B_ _ _ _ _ _ _ _ _ _ _ _
C_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
D_ _ _ _ _ _ _ _ _ _ _ _ _ _
010203040
&A
Page &P
Graph Types
0
0
0
0
0
&A
Page &P
Case1
Colors
00000
00000
00000
&A
Page &P
A
B
C
D
E
GIFIC
000
000
000
000
&A
Page &P
A
B
C
Continuum
0
0
0
0
0
&A
Page &P
Sheet14
0
0
0
0
0
0
&A
Page &P
A
Sheet15
0
0
0
&A
Page &P
Sheet16
Expressive Values of Color
ColorSymbolismAltered ColorSymbolism
YellowUnderstandingMuted Yellowuntruth
Knowledgedistrust
"Heavenly"betrayal
RedPassion
Burning
Blood
BlueNervous SystemShadowy Bluefear
Immortalitysuperstition
Faithgrief
GreenContentmentdecay
Fruitfulness
Hope
VioletPiety
Regal
&A
Page &P
Unusual
Information
&A
Page &P
Head
Xray and Imaging
Respiratory
Routine Blood Work
Cardiac
Gastrointestinal
I/O
Old Lab Work
UniversalLiteral
Line GraphMetaphorVideo
Bar GraphGraphicsPicture
&A
Page &P
&A
Page &P
&A
Page &P
&A
Page &P
-
Tufte, 1997Clinical Data Display
(c) Chris Curran, 2001
Schneiderman Model
DataVisualForm
Data TransformationsVisual MappingsView Transformations
Human Interaction
&A
Page &P
Raw Data
Data Tables
Visual Structures
Views
Task
Bugs
&A
Page &P
Cole DB Single
&A
Page &P
Patient Died
Each Patient = one icon
Metastases
Thick deep extremity primary tumor
Thin deep axial primary tumor
Lymph nodes removed
Cole DB Screen
High
Integrality
Low
LowHigh
Meaningfulness
Male
Female
&A
Page &P
The higher on the vertical scale and the more to the right, the better the graph Cole, 1995
Tufte EMR Single
-1yradmission datetoday's date
result type, medication, etc.most recent value
critically elevated+Note: initials, credentials: date
elevated+Note: initials, credentials: date
normal rangeNote: initials, credentials: date
reduced-
critically reduced-
&A
Page &P
LastName, FirstName, MI Admission Date Today's Date Patient Location
Problem List: Diagnosis 1, diagnosis 2, Diagnosis 3, etc.
More than 1 year prior to admission
One year prior to admission
First week of admission
Today's value
Tufte EMR Screen
Public, John Q.admitted 6/4/996/10/996th Intermediate Care
Right lower lobe pneumonia, new onset diabetes, history of manic depressive disorder
WBC 11100c/ulGlucose 237 mg/dlMood 0
+++Discharge RS MD 1200 6/10/99
+++
[[[Note 2
---
---Note 3
T 98.8* FReg Insulin 3unitsLi .56 mmol/l
+++Note 4
+++
[[[
---
---
R 18 resp/minCa 8.7 md/dlCl 100 mmol/l
+++
+++
[[[
---
---
Cefuroxime 1.5gNa 136 mmol/lCO2 23.7 mmol/l
+++
+++
[[[
---
---
Clindamycin900mcg
+
+
[
-
-
&A
Page &P
Vessey
&A
Page &P
Problem Representation
Problem Solving Task
Mental Representation
Problem Solution
Table Demo
[From Nursing Research (1996), 45(6), pg.354]
Revised Table
Table 1.Summary Statistics of the Independent VariablesTable 1.Summary Statistics of the Independent Variables
VariableMSDMinimumMaximumVariableMSDMinimumMaximum
Total Sample
Weight179.2840.14104300Total Sample
Systolic Blood Pressure130.6816.3993182Cholesterol Level19430107339
Diastolic Blood Pressure77.1111.0152111Weight17940104300
Cholesterol Level194.4430.11107339Systolic Blood Pressure1311693182
Coping Score0.230.45-0.781.41Diastolic Blood Pressure771152111
Type ACoping Score0.20.5-0.81.4
Weight188.5139.16112298
Systolic Blood Pressure138.1616.98107182Type A
Diastolic Blood Pressure79.8311.0454111Cholesterol Level20644112339
Cholesterol Level205.5443.74112339Weight18939112298Revised Table
Coping Score-0.130.31-0.780.97Systolic Blood Pressure13817107182Table 1.Summary Statistics of the Independent Variables
Type BDiastolic Blood Pressure801154111VariableType AType BTotal`
Weight170.5739.22104300Coping Score-0.10.3-0.81.0Mean
Systolic Blood Pressure123.6112.1793155
Diastolic Blood Pressure74.5310.3752102Type BCholesterol Level206184194
Cholesterol Level183.9638.46107316Cholesterol Level18438107316Weight189171179
Coping Score0.570.260.101.41Weight17139104300Systolic Blood Pressure138124131
Systolic Blood Pressure1241293155Diastolic Blood Pressure807577
Diastolic Blood Pressure751052102Coping Score-0.10.60.2
Coping Score0.60.30.11.4Range
Cholesterol Level112-339107-316107-339
Weight112-298104-300104-300
Systolic Blood Pressure107-18293-15593-182
Diastolic Blood Pressure54-11152-10252-111
Coping Score-0.8 to 1.00.1 to 1.4-0.8 to 1.4
&A
Page &P
Correlation Demo
Correlations
(From SPSS)Trial 1Trail 2Trial 3Trial 4
Trial 11.0000.4880.2460.223
Trial 20.4881.0000.8120.803
Trial 30.2460.8121.0000.785
Trial 40.2230.8030.7851.000
**Correlation is significant at the 0.01 level (2-tailed).
RevisedCorrelations
(Option #1)Trial 2Trial 3Trial 4Trial 1
Trial 20.80.80.5
Trial 30.80.80.2
Trial 40.80.80.2
Trial 10.50.20.2
(Option #2)RevisedCorrelations
Trial 2Trial 3Trial 4Trial 1
Trial 20.810.800.49
Trial 30.780.25
Trial 40.22
Trial 1
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Universal Graph
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Universal Graph
1532.13279.21702880
2084169.71542520
2018.4205.951622700
1999.5255.48135.53240
2238.0594942700
2217.25248.42582700
2414.05263.288.252895
2015.2518760.254860
82582.4524900
132.3110.99495040
137199.9128900
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Graph Types
Case1Case2Case3Case4Case5Case6Case5Case2Case1Case4Case3Case6
A354215244910494235241510
B21142341322
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F93519413125
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Colors
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GIFIC
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Sheet16
Expressive Values of Color
ColorSymbolismAltered ColorSymbolism
YellowUnderstandingMuted Yellowuntruth
Knowledgedistrust
"Heavenly"betrayal
RedPassion
Burning
Blood
BlueNervous SystemShadowy Bluefear
Immortalitysuperstition
Faithgrief
GreenContentmentdecay
Fruitfulness
Hope
VioletPiety
Regal
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Unusual
Information
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Head
Xray and Imaging
Respiratory
Routine Blood Work
Cardiac
Gastrointestinal
I/O
Old Lab Work
UniversalLiteral
Line GraphMetaphorVideo
Bar GraphGraphicsPicture
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Color
(c) Chris Curran, 2001
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Why Do We Use Color?Formatting PurposesGroup data (patterns)Create focused attention to specific data (highlight data)Semantic Purpose (encode data)Create RealismAesthetic Purpose (visual appeal)
(c) Chris Curran, 2001
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Color is superior to size, shape or brightness as a mechanism to target a feature in a display When to Use Color
(c) Chris Curran, 2001
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Eleven Colors That Are Never ConfusedWhiteBlackGrayRed GreenYellowBluePinkBrownOrangePurple
Kosslyn, 1994
(c) Chris Curran, 2001
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General Guidelines: Use of ColorUse warm colors in the foregroundHave a large luminance contrast between the foreground and backgroundAdjacent colors should have different levels of brightnessRedundant color coding improves search tasksColor should be a secondary cue (always design for monochrome first) Travis, 1991
(c) Chris Curran, 2001
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Kinds of Color ContrastsLight - DarkCold - WarmContrast of :huesaturationComplimentary Contrast (from color wheel)
(c) Chris Curran, 2001
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General Guidelines: ColorsColors have cultural significance. Each individual sees, feels, and evaluates color in a very personal way.
(c) Chris Curran, 2001
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General Guidelines: ColorsRed: alert values, warningBlue: most easily distinguished but does not photocopy wellOptic Yellow: (a greenish yellow color) most visible to humansShades of Grey: Best for those who are color blind Use Conventional colors (e.g., forests are green)
(c) Chris Curran, 2001
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Take Home MessageHow Data are displayed mattersDisplays should be configured around the data and not how it was obtained
(c) Chris Curran, 2001
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Things to ConsiderHow much data do I have?What cognitive task is needed?Are the data continuous or discrete?Am I making an exact or a relative judgment?Are the data static or dynamic?Are there display conventions about the subject area?Is the domain familiar to the audience?
(c) Chris Curran, 2001
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Current RecommendationsTables: good for small datasets & to depict quantitative data where specific data are neededPie Graphs: good for judging proportionBar Graphs:display change or trends; excellent universal graph (better than line)Icons: good for synthesis of data and meaning; may be best for qualitative (relative) judgmentsShapes / Figures: Best to display integrated data
(c) Chris Curran, 2001
We will attempt to address these questions:(state questions)
The take home message is that configuration of the display matters. If the method of presentation of your data has an format that 1) reduces visual searching, 2) facilitates recognizing patterns and exceptions to those patterns within the data, and, 3) if there is an understood meaning is the style of presentation, extracting information and knowledge tranfer are facilitated.
For example, most of us learned the saying On old olypmus terrace tops a Finn and German vaulted a hedge in order to remember the 12 cranial nerves. Analogy or a metaphor connects meaning and can be used within displays to enhance learning. Smith (1986) conducted a study where subjects were presented with perceptually random stimuli and asked to reproduce them. The reproduced set was then used as a stimulus for the second set of subjects who were also asked to reproduce what they were shown. By the twelfth trial, the stimuli had a definite structure to them. The subjects had imposed a structure on the random events. This experiment demonstrated the human tendency to organize phenomena in order to generate meaning.These are the goals of good display design.
So knowing how we process information and learn, and keeping in mind the goals of better data display, what can we do with displaying data that helps these processes?Today, we are faced with large volumes of data. The challenge is how to extract the information, and ultimately knowledge, available within these data. It is my opinion that three things can assist this process: tools, support systems and data displays. Humans are good analog data processors; computers are good digital data processors.
I will give you a few hints about words but the majority of this presentation focuses on the numeric side of this slide.Heres an example of the impact of font type.Data elements should be grouped by relationships, similarities, and levels of information (elements, subsets or sets).
How do you arrange data to convey meaning? The ways of doing this are:proximity of datause of white spacenavigationAnd you make patterns and exceptions to patterns visible throughroundinglabelling & spacingdisplay format
Redundancy in communication helpsRound to 2 significant or effective digits (not decimel points)If we are interrupted by any task, the number of digits we retain in short term memory generally drops to two (Simon, 1969, Compton lectures)Keep the exact data when performing calculations, round for presentation/reporting of results; can put exact data in an appendixPut figures to be compared in columns rather than in rowsThe design of a table is determined by the data, not the labelsDummy Tables with no data can lead to poor table design[Is this true???? Trends are viewed across rather than down]Add row & column averages (make the main effects explicit)Averages provide visual focus for inspecting the dataOrder rows & columns by sizeShow larger numbers above smaller numbers to make mental subtraction easier.Facilitating mental arithmetic is important when scanning large data sets.Ordering columns makes no differenceA statistical table is not a telephone directory ; labels need NOT be alphabetical or in time sequence (it is easier to find an isolated name than to interpret an isolated number from an unstructured table).
Spacing & layoutFrom Nursing Research Journal (1996).Round to 2 significant or effective digitseliminate leading 0 trailing 0 does not matterPut figures to be compared in columns rather than in rowsAdd row & column averages (make the main effects explicit)Order rows & columns by sizeShow larger numbers above smaller numbers (makes mental subtraction easier)Spacing & layoutsingle spacing guides the eye down the columnuse gaps (white space) between groups (columns or rows) to guide the eye across the data & to cluster datadata meant to be compared should be close togetherFrom SPSS Program: Correlation Statistics (1997).Format often found in the literature.Order of Trials changed to highest correlation to lowest.Only 2 effective digits displayedNo leading 0 displayedCan do a mirror image if desired for redundancy.Why use a graph rather than a table?Also used to trend data, especially dynamic data.
The advantages of analog properties may diminish with increasing size and complexity.Could build analogy into this:five aspects of a problemfive of something that fits together to make a whole.Cleveland & McGill (ordered from most to least accurate)Position along a common scalePositions along nonaligned scalesLength, Direction, AngleAreaVolume, CurvatureShading, Color SaturationBertin (1981)
Integrality relates to the degree to which a pattern is apparent. Meaningfulness relates to the degree to which a pattern, once detected, is interpretable.
Cole believes that these 2 concepts are key to design of a good graph: the goal is high-high.
The ability to estimate requires: ability to discriminate, rank order and to think in ratios.
Implications for Novice to Expert: Interesting and essentially untouched research field?Graph type should maximize accuracy; design for most accurate elemental type.
Very little research on this depiction.
Horizontal bar graphs and line graphs are the most frequently used types of graphs.These are the types of elemental judgments we make.
Position along a common scale
Positions along nonaligned scalesLengthDirectionAngleAreaVolumeCurvatureShadingColor SaturationGoal: Construct a graph that uses elementary tasks as high in the hierarchy as possible (the elements are in hierarchial form, read left to right)
Purpose (for Cleveland) of elementary perceptual tasks is to extract quantitative information from the graph; nothing is stated about qualitative information.
The power of a graph is its ability to enable one to take in quantitative information, organize it, and see patterns and structure not readily revealed by other means. [This may distinguish why sometimes other types of graphs seem to work better; i.e., those graphs may be representing qualitative amounts. ]
How does this apply in practice?
These are the types of graphs with their respect elemental task(s).
You can analyze a graph for its properties and either understand the accuracy of the graph as displayed or try to reduce the display to the most accurate elemental form that retains meaning. Also known as a Pareto Chart (when organized highest to lowest).Need to be careful using disk charts (3D Pie Charts) because they distort the proportion of the pieces and lead to judgment errorsuse flat pie graphs.Cognitive Fit reduces cognitive load.
Cognitive fit between the task (judgment) to be achieved and problem representation is believed to affect the efficiency and effectiveness of decision-making performance (Vessey, 1991). The following table depicts the attributes of cognitive fit:
Spatial / Continuous / Perception / Graphical (Analog)
Symbolic / Discrete / Analysis / Tabular (Digital)
Cognitive fit is achieved when attributes are consistent across each row. If attributes of the problem representation or task are mixed (attributes vary between rows), then the individual must transform the data in order to make a decision. This transformation increases cognitive load.
A picture is worth a thousand words.An example of good semantic mapping
Needs researchBelieved to aide in recall of information
The benefit of metaphor icons is that they have the capacity to carry much greater descritpive information using the same or less physical display space than other forms of presentation.They speed perceptual recognition, reduce cognitive load and are based on association (thus re-inforcing mental mapping).
Elting & Bodey (1991)
ICU Patient Display
The advantages of analog properties may diminish with increasing size and complexity. This may be the case here.Use colors that are wheel separated in the spectrum (not adjacent to each other)For colors juxtaposed, use colors separated by at least one distinct color in the color wheel
Avoid:1) cobalt or deep blue 2) red and blue in adjacent regions
Regions of the same color will be seen as a groupUse deeper saturations and greater intensities for hues to indicate greater amounts
Never use all eleven in one display ; people can only keep nine distinctions in mind at any one point in time
Is there a correst number to use: different opinions (generally 4-5 max).It probably depends on what you are trying to convey with color.
Warmer colors are perceived as closer.
Use the same color throughout to mean the same thing.
For example, white in the US is a good color; while in the middle east, white is used to signify death.Respect cultural norms for colors (In US, red means danger, blue means coolness, and green means life).Avoid blue if the graph is to be photocopied