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Visualizing Qualitative Data.
Transcript of Visualizing Qualitative Data.
Dutch engraver, painter believed that all knowledge can be communicated visually
Thomas Clarkson, 1780s
Etienne Marey 1887,
La Methode Graphique
Scott McCloud, 1993 "Understanding Comics"
an illustrated presentation on data visualization; including a brief timeline of relevant theory, exceptional applications, and ending with a demonstration of best practices, indispensable cloud-based tools, and other amazing curiosities including typos and neologisms.
Leonardo da Vinci, 1490
Vitruvian Man, or
the Canon of Proportions
Joseph Priestly, 1769
A New Chart of History
John Snow, 1854,
Map of Outbreak of Cholera in London
Florence Nightingale, 1858, Diagram of the Causes of Mortality, presented to the queen to secure better military hospitals
Edward Tufte, 1983
"Visual Display of Quantitative Information"
Graphic design emerges as a distinct field of study,a legitimate discipline.
Anton Stankowski, 1967. Visual Presentation of Invisible Processes
Gestalt Principles of Perception
Data viz is nothing new.
At the simplest level, we frequently interact with pie charts, graphs, and maps.
Define data visualization
Review history of applications and theory
Visualizing your Data
Choose the Tool
Give focus to your data
too much information
too much text
Start with the end in mind. What story do you want to tell? Why do you want to tell it? What are you trying to answer?
Validate, justify, contextualize focus. Give focus and purpose to the data, give it structure.
Identify your audience. What message do you want them to walk away with?
to the story
to the principles of design
to the data
Show, don’t tell
Don’t overuse/abuse colors/fonts
Less is more/clean simple design
Consider cultural implications. What icons are familiar? What colors are meaningful?
Consider visual impairments such as colorblindness
a pictorial timeline of sorts
tells a story
(trends, correlations, relationships, outliers, context, narrative, processes)
The story of 2.5 million Michigan voters from last week.
The NYT Election Maps
the amounts of raw data, both qualitative and quantitative, available to us
cloud-based tools, geospatial tools, infographics, text mining tools, analysis tools
metaliteracies on visual and graphic texts in addition to the established disciplines of graphic design
digital projects, interactive web-based publications, wider dissemination
What is new
Overview of technical process
Brief review of tools and best practices
Data viz communicates
Reference & Resources
Evergreen, S. (2014). Presenting data effectively: Communicating your findings for maximum impact. Los Angeles: Sage.
P93.5 E94 2014
Ward, C., & Wilkinson, A. (2006). Conducting meaningful interpretation: A field guide for success. CO: Fulcrum Publishing.
P93.5 .W37 2006
Smiciklas, M. (2012). The power of infographics : Using pictures to communicate and connect with your audience. Indiana: Que Publishing.
P93.5 .S65 2012
Drucker, J. (2014). Graphesis: Visual forms of knowledge production. Cambridge, MA: Harvard University Press.
P93.5 .D78 2014
Marriott, K., Meyer, B., & Workshop on Theory of Visual Languages. (1998). Visual language theory. New York: Springer. P93.5 .V567 1998
Grace, H., Chan, A., & Yuen, W. (2016). Technovisuality: Cultural re-enchantment and the experience of technology. New York : I.B. Tauris.
P93.5 .T433 2016
Evergreen, Stephanie D.H. (2014) Presenting Data Effectively. Los Angeles: Sage Publications.
Data viz adds a new perspective.
Successful data viz
elicits an emotional response, influences policy.
Data viz contributes to solving real-world problems.
w/o data viz
we risk drowning in 0s and 1s
Data viz is always tailored to a specific audience
big data is digestible
Data viz constructs new realities.
Graphic language is necessary when text fails.
era of big data
analysis of framing and reading graphic novels, visual reading.
Transcriptions of interviews
With Qualitative data, text sources may or may not be digitally born
Preparing Data for Analysis
if not digitally born
2.Clean data, make it easier to read.
Reduce noise (clean up typos, etc..) discrepancies, removing unnecessary information to make it easier to read, get better results.
Remove stop words (lemmatizing) or tell the computer to ignore certain variations (stemming).
you will have 4 roles.
: Just like it sounds, taking a text heavy document and visualizing it.
: This kind of infographic is more of a pragmatic tool. A good example is an infographic that explains measurements.
: Gives a choose your own path approach to a story
: Data is visualized chronologically
Presents 2 opposing ideas
: Just the numbers, ma'am
Photo / Data Visual:
Use images from the real world to emphasize a story, focus on the artistic qualities of the story.
Which infographic should you use?
Two parts to today's presentation
Kate's Digital Humanities Guide.
Vanderbilt University Center for Teaching
Educause. 7 things you should know about visualizing date.
Terms can be used interchangeably but...
is automatic and raw data, as you would see in a graph.
contextualize and interpret data and communicate via a visual language.
Data Visualization or an infographic?
Williams, Alex. (2011). 3 views on the difference between data visualization and an infographic.
Adams, Dianna. (2014). 8 types of infographics and which one you should use.
Jan van der Straet
see Drucker, 2014, p. 17.
see Drucker, 2014, p. 27.
Henry d. Hubbard
U.S. National Bureau of Standards
There is a magic in graphs. The proﬁle of a curve reveals in a ﬂash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces. -1939
Fernande Saint Martin, 1987. Semiotics of Visual Language
Edward Tufte, 1983. Visual Display of Quantitative Information.
Other key works from
20th century works
Walter Crane, 1900. Line and Form
We've talked a lot about creating visual knowledge, but what about reading visual language?
image: https://commons.wikimedia.org/wiki/File%3AMarey%2C_La_methode_graphique...%3B%22_sphygmographe_direc %22_Wellcome_L0021094.jpg
Diagram of the Brookes slave ship.
Christopher Schiener, 1626
Sunspots over time
excerpt from Friendly, 2006, pg. 5
Friendly, Michael. 2006. A brief Hostory of Data Visualization. in Handbook of Computational Statistics: Data Visualization. Heidelberg: Springer Verlag.
post data analysis
Not so free data analysis and visualization software
Free text analysis and visualization software
Or hire a graphic designer.
XML editor like Oxygen or XML Notepad.
Text editor software
photo editing software?
1. Identify Text TS Eliot's The Wasteland
2. Digitize text. I am using a version from Project Gutenberg: http://www.gutenberg.org/files/1321/1321-8.txt
3. Clean, mark up text with xml editor. or go through one-stop shopping tool like Voyant.
4. Analyze with your lens in mind.
Kate Langan, Associate Professor University Libraries, Western Michigan University
Late 1800s - Early 1900s
Marshal McCluhan & Quentin Fiore. (1967). The Medium is the Massage
Qualitative data has a place in academic research.
Visualizing that data can be a powerful tool to communicate, influence.
Stay true to the interpretation, focus, or lens that you are using to present your qualitative data.
Know that variables can and will change during qualitative data collection and analysis. It is messy. That is normal.
We are fortunate to have a suite of tools available to us. It is not instant but with a little work, some beautiful documents can be produced.
Follow along: http://tinyurl.com/hxarddt
from Gestalt Psychology
Describes how we organize visual information by grouping parts in order to make a whole.
(There is a funny story about this title.)
Data viz allows for an immediate recognition and connection to the data.
During a data visualization project,
bells and whistles
limited free access
stability, reliability, future access in question, may disappear
Best program may be a matter of preference
Let's Walk through a project.
3.Analyze date (with two software tools)
4.Choose an infographic analogy
Text from student answers defining plagiarism
fear not, no identifying information forthcoming
student work from Spring semester 2015
student work from fall semester 2015
born digital from an online survey
Though born digital, still need to clean it up.
In reading the various plagiarism policies in this chapter, how would you write a plagiarism policy for younger scholars? For more experienced scholars? How does plagiarism change as a scholar grows over time and with experience? How does accountability change?
1. Grab text (literally copy it)
2. Paste it in a text editor (small scale project)
3. Clean up any noise, unnecessary text, typos, etc...
My question to the graduate students was:
What do I want to know?
The variations in language from one semester to the next? On time? Accountability? Adjectives used?
Positive language? Negative language when describing plagiarism?
Do I need more research?
Do I want to add demographics back into the data?
Do I want to know what their undergraduate major was?
I've graded the homework so I already know what to expect at a basic level.
But with larger batches of data, you can let the patterns and relationships emerge by themselves.
Like any other research project, this is also iterative. There may be false starts. Editing of the original analysis lens may need adjusting.
Design and Visualize
Many,may online tools,examples, and help pages
Allow yourself to play around and explore.
Expect a little bit of failure and a lot of frustration.
Ramsay, Stephan."The Hermeneutics of Screwing Around; or What You Do with a Million Books". (2014).in Digital Humanities: Pastplay: Teaching and Learning History with Technology by Kee, K., Michigan:University of Michigan Press.
29 pages of messy data
Cleaned up info
Other software you'll probably need
Expect to work with many different file formats.
Do I want to analyze at the student level? at the class level?
* = brief (and I mean brief) demonstration
These programs typically sit on your machine, not web-based, though have cloud storage.
A Humanist approach
to quantitative research.
Different platforms, devices
Follow along: http://tinyurl.com/hxarddt
talking to us about
close text analysis
organizing, accessing information
Social Sciences background
tech enhanced research