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I N T R O D U C T I O N T O D A T A V I S U A L I Z A T I O N June 18, 2020 2 - 3 : 3 0 p m I N T R O D U C T I O N S Hillary Blevins Martha Larkin, PhD Center for Faculty Excellence Riley College of Education & Leadership 18 years in

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June 18, 2020 2 - 3 : 3 0 p m

I N T R O D U C T I O N T O D A T A V I S U A L I Z A T I O N

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I N T R O D U C T I O N S

https://www.bizarro.com/

Hillary Blevins

Center for Faculty Excellence 18 years in nonprofit field before joining Walden in 2019 Self-taught in data visualization, starting with Excel and learning Tableau Background in historical research, strategic planning, program design and evaluation, survey design and analysis, and data visualization Lives in Morrison, IL with husband and 2 kids (age 12 and 8)

Jim Lenio, PhD

Office of Institutional Research & Assessment Coming up on 14-year Walden work anniversary Produced too many data visualizations to count…many

  • f them were not good.

Background in applied psychology, program evaluation, research methods, survey design, statistics, and assessment. Live in Eagan, MN with wife and 3 kids (age 8, 6, and 4)

Martha Larkin, PhD

Riley College of Education & Leadership Over 40 years’ experience in public and higher education, research, and consulting. Joined Walden in 2009. Background in home economics, special education, and educational assessment. Has numerous publications and presentations on data visualization, assessment, special education pedagogy, and reading. Lives in Miramar Beach, FL with her husband and co- presenter, Dick.

Dick Larkin, PhD

School of Public Policy & Administration Over 40 years’ experience in higher education, research, and consulting. Joined Walden in 2009. Background in urban & regional planning, public admin/public affairs, geographic information systems, and mgmt training. Has published and presented on data visualization and innovative research methodologies. Lives in Miramar Beach, FL with his wife and co- presenter, Martha.

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P R I N C I P L E S F O R D A T A V I S U A L I Z A T I O N

Data guides the visualization type Design matters Focus on your audience Words can be visualized Avoid deception

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DATA GUIDES THE VISUALIZATION CHOICE

C O M P A R I S O N S C H A N G E O V E R T I M E P R O G R E S S T O A G O A L

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C O M P A R I S O N S

35% 25% 17% 15% 8%

Service Fees United Way Grants Fundraising Campaigns Donations

Service fees provide the greatest income stream, but community support makes up nearly two-thirds

  • f all income.

4 15 12 11 8

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

The pilot program served a total of 50 students.

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C O M P A R I S O N S

78% 22% The majority of clients had previous experience with our services.

Returning Clients New Clients 0% 20% 40% 60% 80% 100% I will recommend the program to

  • thers.

I plan to continue with the program. I am satisfied with the program.

Most participants agree or strongly agreed with the key performance indicators for participant satisfaction.

Strongly Agree Agree Disagree Strongly Disagree

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C H A N G E O V E R T I M E

80% 90% 74% 80% 70% 76% 66% 60% Pre-Test Post-Test

Achievement increased during the program for most outcomes.

Outcome 1 Outcome 2 Outcome 3 Outcome 4 800 825 850 875 900 925 950 975 1000 2015 2016 2017 2018 2019

After a dip in 2016, membership has rebounded over the last three years.

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P R O G R E S S T O A G O A L

6204 3461 1149 7000 5000 3000

Scholarships Technology Program Equipment

Fundraising campaigns have been most successful at securing donations for scholarships.

14% 8% 10% 11%

Washington Adams Jefferson Madison

Most elementary school sites have greater market share than last year's average of 9%.

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DESIGN MATTERS

P R E - A T T E N T I V E A T T R I B U T E S U S E C O L O R P U R P O S E F U L L Y L E S S I S M O R E

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D E S I G N M A T T E R S : P R E - A T T E N T I V E A T T R I B U T E S

Few, 2004, p. 5

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Actual chart from the state of Georgia’s COVID-19 tracker.

D E S I G N M A T T E R S : U S E C O L O R P U R P O S E F U L L Y

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Color Palettes & Accessibility

D E S I G N M A T T E R S : U S E C O L O R P U R P O S E F U L L Y

https://color.review/

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Choose Contrasts Wisely

D E S I G N M A T T E R S : U S E C O L O R P U R P O S E F U L L Y

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

(Excel default) (Data label font changed to white) (Dark outline, shaded chart area) (Shaded chart in total) (Black bars, font changed to black, removed lines)

25% 75% 0% 20% 40% 60% 80% 100% Age 24 or Under Age 25 or Older

Participant Age

(Color aligned to palette)

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Walden Examples of Color Palette Use

D E S I G N M A T T E R S : U S E C O L O R P U R P O S E F U L L Y

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Strategies to make data easier to interpret

D E S I G N M A T T E R S : L E S S I S M O R E

10 20 30 40 50 Midwest Southwest West Southeast Northeast

Avg Number of Minutes to say Goodbyes by Region

Sort data from largest to smallest so reader doesn’t have to.

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D E S I G N M A T T E R S : L E S S I S M O R E

45 13 10 7 3 Midwest Southwest West Southeast Northeast

Avg Number of Minutes to say Goodbyes by Region

Consider removing gridlines and axis labels. Data labels can be a useful addition in many cases.

10 20 30 40 50 Midwest Southwest West Southeast Northeast

Minutes U.S. Region

Avg Number of Minutes to say Goodbyes by Region

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D E S I G N M A T T E R S : L E S S I S M O R E

Avoid more than 4 lines in a chart. Line charts are typically used to show trend over time. If possible, put labels near line rather than use a legend.

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FOCUS ON YOUR AUDIENCE

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Clients & General Community Board, Leadership, Funders Operational Decision Makers

V I S U A L I Z A T I O N F O R D I F F E R E N T A U D I E N C E S

Highlights of successes, points

  • f pride, little to no detail

Summary of accomplishments & challenges, trend data Decision making data, granular, detailed

Summer Program Attendance by Neighborhood Neighborhood 2015 2016 2017 2018 2019 Erickson 88 85 60 80 94 Minnehaha 102 90 70 75 101 Hiawatha 31 64 70 80 92 Total 221 239 200 235 287

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A U D I E N C E : C L I E N T S & G E N E R A L C O M M U N I T Y

elementary aged participants in 2019

287

Number & Icon Single Large Number

https://foodgrainsbank.ca/wp-content/uploads/2015/08/Screen-Shot-2015-08-13-at-1.59.14-PM.png https://www.aqua.org/blog/2013/october/happy-world-octopus-day

287

Participants in 2019

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Trend data’s go-to chart: the line chart

A U D I E N C E : B O A R D , L E A D E R S H I P , F U N D E R S

221 239 200 235 287 2015 2016 2017 2018 2019

Program attendance is at its highest in 5 years

221 239 200 235 287 2015 2016 2017 2018 2019

Enrollment goals met for 3 of past 5 years

Target: 230

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Same data table used to create visuals for different audiences.

A U D I E N C E : O P E R A T I O N A L D E C I S I O N M A K E R S

20 40 60 80 100 120 2015 2016 2017 2018 2019

Attendance by Neighborhood

Minnehaha Hiawatha Erickson 31 64 70 80 92

20 40 60 80 100 120 2015 2016 2017 2018 2019

Hiawatha neighborhood attendance increase

Hiawatha

Summer Program Attendance by Neighborhood Neighborhood 2015 2016 2017 2018 2019 Erickson 88 85 60 80 94 Minnehaha 102 90 70 75 101 Hiawatha 31 64 70 80 92 Total 221 239 200 235 287

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WORDS CAN BE VISUALIZED

S T R A T E G I E S F O R S H O W I N G Q U A L I T A T I V E D A T A

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W O R D S C A N B E V I S U A L I Z E D

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W O R D S C A N B E V I S U A L I Z E D

“I just love to visualize data and tell our story!”

90% of client emails were answered within 24 hours

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AVOID DECEPTION

I N A P P R O P R I A T E S I Z E C O M P A R I S O N S Y A X I S M A N I P U L A T I O N U S I N G 3 D C H A R T S

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Most deception is unintentional, which means individuals must be very intentional in the data they are displaying.

R E C E N T N E W S H E A D L I N E S

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When displaying data to be used to make significant decisions…be very explicit. In this case the data was used to make decisions about reopening the state.

R E C E N T N E W S H E A D L I N E S

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75% = 5 books 78% = 10 books 79% = 14 books 80% = 15 books 81% = 16 books 82% = 16 books

I N A P P R O P R I A T E S I Z E C O M P A R I S O N S

https://twitter.com/ObamaWhiteHouse/status/677242822920151045/photo/1

75% 75% 78% 79% 80% 81% 82% 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14

High School Graduation Rate

70% 72% 74% 76% 78% 80% 82% 84% 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14

High School Graduation Rate

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Y A X I S M A N I P U L A T I O N

77 81 85 79 83 72 74 76 78 80 82 84 86 Monday Tuesday Wednesday Thursday Friday

High Temperatures

77 81 85 79 83 20 40 60 80 100 Monday Tuesday Wednesday Thursday Friday

High Temperatures

$2.08 $1.99 $2.57 $- $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 2010 2011 2012

Cost of Peanut Butter

$2.08 $1.99 $2.57 $1.75 $1.85 $1.95 $2.05 $2.15 $2.25 $2.35 $2.45 $2.55 $2.65 2010 2011 2012

Cost of Peanut Butter

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Unable to know which bar is higher. Extra lines in Y axis. Alters X axis labels.

U S I N G 3 D C H A R T S

20 40 60 80 100 Monday Tuesday Wednesday Thursday Friday

High Temperatures

0% 20% 40% 60% 80% 100% 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14

High School Graduation Rate 77 79 75% 75%

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R E S O U R C E S

Books

  • Effective Data Visualization: The Right Chart for the Right Data (Stephanie Evergreen)
  • Presenting Data Effectively: Communicating Your Findings for Maximum Impact

(Stephanie Evergreen)

  • Show Me the Numbers: Designing Tables and Graphs to Enlighten (Stephen Few)
  • The Visual Display of Quantitative Information (Edward Tufte)
  • Storytelling with Data (Cole Nussbaumer Knaflic)
  • The Big Book of Dashboards (Steve Wexler, Jeffery Shaffer, and Andy Cotgreave)
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R E S O U R C E S

Websites

  • https://www.visualisingdata.com/
  • https://flowingdata.com/
  • https://junkcharts.typepad.com/
  • https://www.visualcapitalist.com/

Printable Guides

  • Quantitative Chart Chooser (Stephanie Evergreen)
  • Qualitative Chart Chooser (Stephanie Evergreen)
  • Data Visualization Checklist (Stephanie Evergreen & Ann K. Emery)
  • Evaluation Report Layout Checklist (Stephanie Evergreen)
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Q U E S T I O N S ?

Hillary Blevins Hillary.blevins@mail.waldenu.edu Jim Lenio, PhD Jim.lenio@mail.waldenu.edu Martha Larkin, PhD Martha.larkin@mail.waldenu.edu Dick Larkin, PhD Dick.larkin@mail.waldenu.edu