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Visualizing Data

CSSS 569

Good visual displays uncover patterns quantitative scientists might otherwise miss, and can make or break a paper. This course takes the design of graphics and tables seriously, and surveys a variety of visual techniques for exploring data and summarizing statistical models. Emphasis on principles of effective visualization, novel visual displays, examples from the social sciences, and implementation of recommended techniques in R.

CSSS 569

Visualizing Data

Offered most Winters at the
University of Washington



Winter 2014

Class meets:
TTh 4:30-5:50 pm
Electrical Engineering Building 037

Lectures                 PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser.

Short Course

Visualizing Model Inference and Robustness


Supplementary material: This is a 9-hour short course version of the full Data Visualization course. (The lectures for the full term course are below.) Students taking the short course may also need this brief introduction to R for data visualization.

Materials for the R review session: R code and data for the GDP example. R code and data from the fertility example. Detailed instructions for downloading, installing, and learning my recommended software for quantitative social science are here.

Materials for Session 3: R code and data for the voting example, and sample output for expected values, first differences, relative risks, and a combination plot. R code and data for the inequality scatterplot, and sample output.

Materials for Session 5: R code for the crime example.

Topic 1

Course Introduction


Topic 2

Principles for the Visual Display of Scientific Information, Part I


Topic 3

Principles for the Visual Display of Scientific Information, Part II


Topic 4

Cognitive Issues in Visualization, Part I: Everything but color


Topic 5

Cognitive Issues in Visualization, Part II: Color


Topic 6

Graphical Programming, part 1: Using R graphics functions


Supplementary material: In class, we will discuss the use of heatmaps to explore complex multivariate data. Supplemental color and printable slides use heatmaps to explore dyadic relationships in trade data.

Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.

Topic 7

Graphical Programming, part 2: R graphics from the ground up


Supplementary material: We will examine two R scripts: a script using the base R graphics to show confidence intervals around a regression line, and another script using grid R graphics to accomplish the same task. Both scripts require this dataset.

Topic 8

Exploratory Data Analysis: The border between exploration and modeling


Topic 9

Visualizing Inference: Introduction to the tile package


Supplementary material: The tile package and a variety of demo scripts and examples can be found under the Software tab at left.

Topic 10

Visualizing Robustness


Student Assignments

Problem Set 1

Due in class January 21

Supplementary material: You will need these data.

Problem Set 2

Due in class February 6

Problem Set 3

Due in class March 11

Breakout Group

Individual memo due before group meets; Group memo due by February 24

Students will join a small group to discuss a visual display problem of common interest; creation and organization of these groups to be coordinated through the web. Students will write a 1-2 page memo before the first group meeting, and each group will write a 5+ page essay for the class on what they have learned, to be distributed by February 24. Groups will answer questions from the class during the week of February 24. See the syllabus for further details.

Final Poster

Presented in class during the final two weeks

On an assigned day during the last two weeks of the course, each student will present a poster applying the tools learned in class to their own research. Alternatively, students can take an article published in their field and show how better visuals would either more clearly convey the findings or cast doubt on them. The final presentation may address problems raised in the breakout session or problem sets, but it is usually more fruitful for students to tackle a new problem.

University of Washington linkDepartment of Political Science
Center for Statistics and the Social Sciences
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Design: Chris Adolph
and Erika Steiskal

Jefferson (2007-2011)