Many debates and questions in political science can be addressed with data: How does money influence legislation and elections? Are economically developed countries more democratic? What are the causes of war? There is now much more data available that might address these debates. This course provides tools for the first steps in using data to answer political science questions: obtaining, cleaning, transforming, analyzing, and visualizing data.
Methodologically, the class would be considered preliminary to Political Science 328 (Statistical Analysis). In 328, we learn that the first step in any data analysis is to look at your data. (Then you apply various statistical tools.) This class is about how you look at your data, and how you display that data to others. Previously. this was known as exploratory data analysis. Now, it is called data visualization. Thus, this class is not a statistics class: It neither requires nor imparts any statistical background. It is a methods class.
The BYU Department of Political Science has developed a set of expected student learning outcomes. These will help you understand the objectives of the curriculum in the program, including this class. In the parlance of the Political Science department's learning outcomes, this course helps you:
I regularly make announcements, clarifications, further instructions, etc., in class and by email and Learning Suite. You are responsible for all of these, even if you do not attend class. You are also responsible for keeping your email up to date at my.byu.edu. (You should let me know if your email changes during the semester.) I suggest that you exchange phone numbers and/or e-mail addresses with other students in the class.
I will hold office hours Mondays and Wednesdays from 4-5 p.m. I am also available at other times if you make arrangements with me. I encourage you to come by to talk about assignments in the class, suggestions for improving the class, politics and current events, the perils of student life, or for any other reason.
I expect that students will have a working knowledge of politics and basic political science, and know how to organize and write. This can easily be fulfilled by taking the department core courses (Political Science 110, 150, 170, 200).
The format of the course is unusual: the lectures are online here, and I will meet with the students in a small computer lab to work through exercises and start on the homework. Please be ready to start on the computers (i.e. logging in, etc.) when class begins. Completing the exercises will require having listened to and viewed the lectures prior to class, but students may do so at whatever time is convenient.
The lectures are recorded in QuickTime format, which should be viewable on nearly any computer or mobile device. One advantage to the format is that, on most devices, lectures can be sped up to 1.4x to 1.5x; beyond about 2x, most of them become unintelligible. Students are advised to take advantage of this feature, as humans can generally understand speech at a higher rate than they can produce it. Be advised, though, that higher speeds generally require more focused attention, as important details are easier to miss. ItŐs also possible to rewind and slow the recording back down if a particular section moves quickly or is difficult to understand.
It is not easy to view the lectures at higher speeds within iTunes or even QuickTime. Here is one way to do it on a Mac. (A similar method can be used, with appropriate modifications, on Windows.) Within iTunes (or iTunes U), Subscribe to the Data Visualization course, which will download the files to your computer. Click on Library (top center), and find the "Data Literacy and Data Visualization" course (possibly by clicking iTunes U in the Library on the left). Right-click on any Data Visualization lecture there, and click on "Show in Finder". That should open up the directory where the lectures are stored. Right-click on the lecture you wish to view (say, Lecture 0), and open it with VLC (or some other media program that you like). Within VLC, click on the Playback tab, and modify the playback speed. If you like the standard playback speed, you can just watch the lectures in iTunes U.
A Chinese proverb (supposedly) says, "I hear and I forget, I see and I remember, I do and I understand." This philosophy drives the requirements of the class.
All assignments are due at the beginning of class, usually on Tuesdays. I will not accept late assignments. The primary reason for no late assignments is so that we can discuss the assignment in class immediately after it is turned in. If you anticipate a problem with submitting an assignment when it is due, speak to me before the assignment is due so that we can try to work out an alternative arrangement.
Each week you will be required to complete assignments in which you practice various data visualization skills. Generally, weekly assignments will be posted on Learning Suite after class on Tuesday. (I will record scores on assignments and exams in Learning Suite as well.)
You may work together on these assignments (in groups of two or at most three), but you must write up your answers separately. I give much more detailed instructions on how to report your work together in the Academic Honesty section below. Generally, if you use other persons' work, or make changes to your own work without inquiring or understanding what you did incorrectly, then you are trying to get a grade using someone else's knowledge. Giving or receiving answers in this manner is not permitted in this course. If you do not learn how to analyze or solve problems on your own, you will have difficulty on the exams and research project.
Each student will present a poster on a topic of her/his choosing. The project will allow you the opportunity to apply the skills that we will develop in this class to actual data and questions. Each student will find a dataset in her/his areas of interest that is not already used in the course, analyze it to assess the structure of the data, and work through the most appropriate, succinct, and informative summaries and visualizations. Class time at the end of the semester wil be used to work on the research project. The research project is designed to mimic the sort of tasks one may be given in a corporate/government/scientific work environment.
I strongly recommend that you consult with me through all phases of your research. I may be able to help you select a feasible topic, find data, or comment on your visualization.
Preliminary work for your poster will be assigned as parts of problems sets.
You will present the poster at the Mary Lou Fulton Conference. Details can be found here. The submission deadline is March 30 at 12 noon.
The course is graded on a modified curve. The basic idea is that I will look for natural breaks between students and assign grades accordingly. Thus, I do not assign a set number or percentage of As and Bs.
I include the following information from the BYU 2016-2017 Undergraduate Catalog, which guides how I grade and determine workload:
Putting these statements together, the university expects an "average student" to work "much more" than 9 hours a week to receive an 'A' (= "excellence") in a 3 credit-hour course. This is my expectation as well.
From the Academic Honesty section of the BYU Honor Code: "The first injunction of the BYU Honor Code is the call to `be honest.' Students come to the university not only to improve their minds, gain knowledge, and develop skills that will assist them in their life's work, but also to build character. `President David O. McKay taught that character is the highest aim of education' (The Aims of a BYU Education, p. 6). It is the purpose of the BYU Academic Honesty Policy to assist in fulfilling that aim."
"BYU students should seek to be totally honest in their dealings with others. They should complete their own work and be evaluated based upon that work. They should avoid academic dishonesty and misconduct in all its forms, including but not limited to plagiarism, fabrication or falsification, cheating, and other academic misconduct" (cite). Read the full version here.
A colleague (Mitch Sanders, former professor at Notre Dame) has already explicated these issues specifically for political science. Please read here.
In this class, you need to acknowledge the contributions of others toward your assignments. I have taken the following guidelines from MIT's Unified Engineering class. I have changed and added various words where appropriate:
"The fundamental principle of academic integrity is that you must fairly represent the source of the intellectual content of the work you submit for credit. In the context of [Poli 328], this means that if you consult other sources (such as fellow students, TA's, faculty, literature) in the process of completing homework [(or Stata codes)], you must acknowledge the sources in any way that reflects true ownership of the ideas and methods you used."
"Doing homework helps to engage with the concepts and material taught in class on a deeper level. To enhance the learning process we strongly suggest that you first try to solve the problems by yourself and then discuss challenges in groups or in office hours if necessary. Discussion among students and in office hours to digest the material and the homework problems or to prepare for [exams] is considered useful in the educational process. COLLABORATION ON HOMEWORK IS ALLOWED UNLESS OTHERWISE DIRECTED AS LONG AS ALL REFERENCES (BOTH LITERATURE AND PEOPLE) USED ARE NAMED CLEARLY AT THE END OF THE ASSIGNMENT. Word-by-word copies of someone else's solution or parts of a solution handed in for credit will be considered cheating unless there is a reference to the source for any part of the work which was copied verbatim. FAILURE TO CITE OTHER STUDENT'S CONTRIBUTION TO YOUR HOMEWORK SOLUTION WILL BE CONSIDERED CHEATING."
"Study groups are considered an educationally beneficial activity. However, at the end of each problem on which you collaborated with other students you must cite the students and the interaction. The purpose of this is to acknowledge their contribution to your work. Some examples follow:
Unfortunately, some students still profess ignorance of or attempt to find loopholes in the previous guidelines. As a result of sad experience, I repeat the following guidelines and add clarifications:
As required by Title IX of the Education Amendments of 1972, the university prohibits sex discrimination against any participant in its education programs or activities. Title IX also prohibits sexual harassment-including sexual violence-committed by or against students, university employees, and visitors to campus. As outlined in university policy, sexual harassment, dating violence, domestic violence, sexual assault, and stalking are considered forms of "Sexual Misconduct" prohibited by the university.
University policy requires any university employee in a teaching, managerial, or supervisory role to report incidents of sexual misconduct that come to their attention through various forms including face-to-face conversation, a written class assignment or paper, class discussion, email, text, or social media post. If you encounter Sexual Misconduct, please contact the Title IX Coordinator at firstname.lastname@example.org or 801-422-2130 or Ethics Point at https://titleix.byu.edu/report or 1-888-238-1062 (24-hours). Additional information about Title IX and resources available to you can be found at http://titleix.byu.edu.
Brigham Young University is committed to providing a working and learning atmosphere that reasonably accommodates qualified persons with disabilities. If you have any disability which may impair your ability to complete this course successfully, please contact the University Accessibility Center (UAC), 2170 WSC or 422-2767. Reasonable academic accommodations are reviewed for all students who have qualified, documented disabilities. The UAC can also assess students for learning, attention, and emotional concerns. Services are coordinated with the student and instructor by the UAC. If you need assistance or if you feel you have been unlawfully discriminated against on the basis of disability, you may seek resolution through established grievance policy and procedures by contacting the Equal Employment Office at 422-5895, D-285 ASB.
All course materials (e.g., outlines, handouts, syllabi, exams, quizzes, PowerPoint presentations, lectures, audio and video recordings, etc.) are proprietary. Students are prohibited from posting or selling any such course materials without the express written permission of the professor teaching this course. To do so is a violation of the Brigham Young University Honor Code.
Mental health concerns and stressful life events can affect students' academic performance and quality of life. BYU Counseling and Psychological Services (CAPS, 1500 WSC, 801-422-3035, caps.byu.edu) provides individual, couples, and group counseling, as well as stress management services. These services are confidential and are provided by the university at no cost for full-time students. For general information please visit https://caps.byu.edu; for more immediate concerns please visit http://help.byu.edu.
In addition to the lectures, all readings should be read before class for full understanding of the subject material.
There are two required textbooks that are available for purchase at the BYU bookstore, or your bookstore of choice.
Students are likely to find learning R to be the most challenging part of this course. It is also, in the long run, the most rewarding, in that it is a valuable and marketable skill that also prepares you for more advanced classes. The learning curve is steep, however, and it is best to be prepared. Besides the R Cookbook above, students may wish to bookmark, or just download and print out, Tom Short's R Reference Card. I also like the documentation at UCLA's Statistical Computing pages and Princeton's Data and Statistical Services.
Lecture 1: Introduction. Nature of the course, its relationship to other courses and students' plans of study; how and why data visualization can be useful.
Lecture 2: Data are All Around Us! Terminology, sources, and advice on what to do if you cannot find a preexisting dataset. (Yau, ch. 2)
Lecture 3: Data Tools, part I. Open-source solutions that require little or no additional information or effort to produce compelling results.
Lecture 4: The Good. Exemplary representations of data from our field and others, with a discussion of what makes them so compelling. (Yau, ch. 1 up to "Design")
Lecture 5: The Bad and The Ugly. Ideas that seemed good at the time, why they really are not, how we can fix them, and what general principles they imply. (Yau, rest of ch. 1)
Lecture 6: Data Tools, part II. These versatile tools allow you to upload, explore, and visualize your own data. (Yau, ch. 3 up to "Illustration")
Lecture 7: Truth. Data visualization is inherently a reduction of information. The main imperative, in reducing information, is integrity: representing the data as honestly as possible. We look at some good cases and some not-so-good cases. (Yau, rest of ch. 3)
Lecture 8: Introduction to R. A powerful (and free) general statistical package, R is capable of producing a wide range of graphs. In these lectures we demonstrate how to download and install the package, add libraries, and import data. (Teetor, chs. 1-4)
Lecture 9: Beauty. The difference between a fairly good data visualization and an amazing one often lies in the application of a few straightforward graphical principles. (Teetor, ch. 5)
Lecture 10: R, part II. Data structures and data transformations. (Teetor, chs. 6-7)
Lecture 11: Time. Different ways of examining variables over time. (Yau, ch. 4)
Lecture 12: Plots in R. Scatterplots, line graphs, boxplots, dot charts, and more. (Teetor, chs. 8-10)
Lecture 13: APIs. How to connect to a vast array of public data sources.
Lecture 14: Space. Less common, but more eye-catching, are ways of looking for patterns across space, especially in maps. (Yau, pp. 80-89)
Lecture 16: Space and Time. Representing relationships and trends over both space and time. (Yau, pp. 271-308)
Lecture 17: Research Design. Experiments, natural experiments, regression discontinuity, and difference-in-difference designs, with applications. (Yau, ch. 5)
Lecture 19: Workshop -- Election 2016. Why are political scientists so good/bad at predicting election outcomes? (Yau, ch. 6)
Lecture 20: Workshop -- Partisanship. Testing hypotheses on partisanship in American politics.
Lecture 21: Monte Carlo. Using Monte Carlo simulations the calculate uncertainty. (Teetor, chs. 11-13)
Lecture 22: Introduction to Shiny. How to build a Shiny App. (Shiny written tutorial: Lesson 1, Lesson 7)
Lecture 23: Introduction to Shiny. How to customize reactions. (Shiny written tutorial: Lesson 4, Lesson 5, Lesson 6)
Lecture 24: Introduction to Shiny. How to customize appearance. (Shiny written tutorial: Lesson 2, Lesson 3)
Lectures 25: Workshop -- Other topics. (Yau, ch. 9.)
The schedule is subject to more change than usual as it is the first time I am teaching the courses, and to follow the abilities and interests of the class.
|Date||View Before Class||Read Before Class||Assignments|
|January 10||Lectures 0, 1, 2a, and 2b||Yau: Introduction|
|12||Lectures 2c, 2d, 2d1, and 2e||Yau: ch. 2||Assignment 0|
|17||Lecture 3||Assignment 1|
|19||Lecture 4||Yau: pp. 1-12|
|24||Lecture 5||Yau: pp. 13-20||Assignment 2|
|26||Lecture 6||Yau: pp. 53-75|
|31||Lecture 7||Yau: pp. 76-89||Assignment 3|
|February 2||Lecture 8||Teetor: chs. 1-4|
|7||Lecture 9||Teetor: ch. 5||Assignment 4|
|9||Lecture 10||Teetor: chs. 6-7|
|14||Lecture 11||Yau: ch. 4||Assignment 5|
|16||Lecture 12||Teetor: chs. 8-10|
|21||No class: University Monday|
|28||Lecture 14||Yau: pp. 80-89||Assignment 6|
|March 2||Lecture 16||Yau: pp. 271-308|
|7||Lecture 17||Yau: ch. 5||Assignment 7|
|9||Lectures 18a, 19||Yau: ch. 6|
|14||Lecture 20||Yau: ch. 7||Assignment 8|
|16||Lecture 21||Teetor: chs. 11-13|
|21||Work on poster||Assignment 9|
|23||Work on poster|
|28||Data Analytics with Watson|
|30||Lecture 22||Shiny: 1,7||Poster Due: 12 noon|
|April 4||Lecture 23||Shiny: 4,5,6|
|6||Lecture 24||Shiny: 2,3|
|11||Lecture 25||Yau: ch. 9||Assignment 10|
|13||No class: Fulton Conference|
|18||Work on poster||Revised Poster|
With his permission, this syllabus is largely based on Bear Braumoeller's syllabus. I also consulted the syllabi of Dogan Arslan & Ertan Toy, Skyler Cranmer, Andrew Gelman, Jeffrey Heer, Yusaku Horiuchi, and Kosuke Imai.
Jay Goodliffe's home page
This page is http://goodliffe.byu.edu/306/syllabus.htm