Political Science 328
Quantitative Political Methodology

Course Syllabus
Fall 2012

Lectures: TR 1:35 - 2:50 p.m. 2111 JKB
or TR 3:00 - 4:15 p.m. 2111 JKB
Computer Labs: F 9:00 - 9:50 a.m. 102 or 105 SWKT
  F 10:00 - 10:50 a.m. 102 or 112 SWKT
  F 11:00 - 11:50 p.m. 102 or 112 SWKT

Instructor: Jay Goodliffe
Office: 734 SWKT
Office Hours: MW 3-4 p.m., and by appointment
Phone: 801.422.9136
e-mail: goodliffe@byu.edu

Teaching Assistants:
Bethanie Thatcher b2malan@cox.net T6, W12, W1: 174 SWKT
Brandall Nelson brandallnelson@hotmail.com T9, T7p, T8p: 174 SWKT
Branden Kartchner kartchner10@gmail.com W8, R7a, R8a: 174 SWKT
Cindy West cindywest343@gmail.com M4, W3, W4: 174 SWKT
Stetson Drolet stetsonpd@gmail.com M2, W10, W11: 174 SWKT
Sydney Outzen sydneyoutzen@gmail.com T5, W6, W7p: 174 SWKT

TA Office Hours:
Monday: 2, 4
Tuesday: 9, 5, 6, 7, 8
Wednesday: 8, 10, 11, 12, 1, 3, 4, 6, 7
Thursday: 7a, 8a


Office Hours
Learning Outcomes
How to Succeed in this Course
Computer Labs
Academic Honesty and Plagiarism


I regularly make announcements, clarifications, further instructions, etc., in class and by email. 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.

Office Hours

I will hold office hours on Mondays and Wednesdays 3-4 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. (Suggested topics: playing the organ, practicing yoga, soccer, student evaluations, Choose to Give program, BYU tuition.)

The TA office is located in 174 SWKT and the meeting area for TA consultations is right outside in 173 SWKT. The names and contact information for each TA are listed above.

Learning Outcomes

This course explores the fundamental concepts of empirical analysis in political science, with a heavy emphasis on regression analysis. This course is designed to help you

As a result of its recent accreditation experience (and increasing emphasis from the Department of Education to measure educational outcomes, e.g. NCLB), each program at BYU 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 develop the skills of:

This course also fulfills the General Education Languages of Learning requirement. As noted in that requirement's Foundation Document, this course "prepares [students] to explain 'the world in quantitative terms; to interpret numerical data; and to evaluate arguments that rely on quantitative information and approaches' (Aims of a BYU Education). Students should be able to use the fundamental principles of and fluency in quantitative language as used in modern practical problem-solving situations." As a General Education course, this class also fufills University Core Learning Outcomes:


PlSc 200 is a prerequisite for this course. PlSc 200 teaches basic statistical concepts, as well as writing and research techniques. This course builds on those concepts and assumes you know those techniques; without the prerequisite, it will be (more) difficult to succeed in this course. If you have not taken PlSc 200, take this course after you have. (You may not enroll concurrently in PlSc 200.)


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.

Weekly Assignments


Midterm Exam


Final Exam


All assignments are due on Thursdays at 1:30 p.m. in the Political Science Assignment Drop Box (located outside the entrance to the department office at 745 SWKT). If you have some sort of emergency, you can also turn in your assignment to me electronically (via email), after which you will turn in a hard copy as soon as possible. Assignments must be typed and usually separated into four different packets, as directed on the assignment directions. I will not accept late assignments. Do not ask for exceptions. The primary reason for no late assignments is so that we can discuss the assignment in class immediately after it is turned in. Since everyone has difficulties at one time or another, I will drop the two lowest assignments for the semester. I suggest you save your dropped assignments for when you have a good excuse for missing.

Weekly Assignments

To understand statistics, you must use statistics. Each week you will be required to complete assignments that will include a variety of activities ranging from statistical theory problems to analyzing data and interpreting statistical results. Part of each assignment is explaining concepts and results to a lay audience. Generally, weekly assignments will be posted on the above link after class on Thursday. (We will record scores on assignments and exams in Gradebook.)

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.

You will need to turn in your assignments with precise identification information. Most importantly, pay attention to the instructions at the beginning of each assignment. Your name, PlSc 328, course section, lab instructor name, assignment #, and part # should clearly be indicated at the top of each document. For example:
     Your name
     Political Science 328
     Your section (1, 2, 3, ....)
     Lab instructor: Drolet, Malan, etc.
     Assignment #
     Part #
     Answers begin here...


There is a midterm and final exam. These are both take-home exams. They will require you to solve problems similar to case studies in the weekly assignments. You are not allowed to consult with anyone on these take-home exams. The midterm exam is taken home for a week, and is in place of the weekly assignment. (There is no class during the midterm exam.) The final exam will cover material for the whole semester. It will be distributed on the first day of finals, and is due at the end of finals period. Although this is different than most exams taken during the final exam period, the chair and dean have reviewed this plan, and state that it is within university policy.

Research Project

Unlike previous semesters of PlSc 328, we do not have a research project.

How to Succeed in this Course

The course is graded on a modified curve, using statistical principles that will be explained in more detail in the course. 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, Bs, and Cs.

I will award the higher of two grades:

  1. your overall point total
  2. your point total doubling the weight of the final exam

To use #2, you must complete at least 10 of the 12 weekly assignments (Assignment 0 does not count) and the midterm exam. (Turning in a sheet of paper with your name on it is not completing the assignment. Completing an assignment means working on each part of the assignment.) This allows students who take longer to get the material to still do well in the class. However, if you do not work on the weekly assignments, you will not do well on the final exam.

I include the following information from the BYU 2012-2013 Undergraduate Catalog, which guides how I grade and determine workload:

"The grade given in a course is the teacher's evaluation of the student's performance, achievement, and understanding in that subject as covered in the class. The following adjectives indicate the meaning of the letter grades:
A Excellent
B Good
C Satisfactory
D Minimum passing
E Unacceptable
"Hence, the grade A means that the student's performance, achievement, and understanding were excellent in the portion of the subject covered in the class.
"There are prerequisites that qualify students to be admitted to the more advanced classes offered by a department. A senior has added experience, understanding, and preparation and, consequently, progresses in courses that would have been impossible when the student was a freshman. The level of performance, achievement, and understanding required to qualify for each grade that carries credit (any grade other than E, UW, I, IE, or WE) is higher in a more advanced class than in those classes that precede it, and the student is prepared to work at this higher level" (cite).
"The expectation for undergraduate courses is three hours of work per week per credit hour for the average student who is appropriately prepared; much more time may be required to achieve excellence" (cite).

Putting these statements together, the university expects an "average student" to work "much more" than 12 hours a week to receive an 'A' (= "excellence") in a 4 credit-hour course. This is my expectation as well.

As in many other subjects, learning statistics requires that you are exposed to it multiple times. Do not expect to understand everything in the book the first time you read it. Do not expect to master the material after coming to a single lecture. It takes reading, hearing, and applying the material to grasp the concepts.

Students who have succeeded in this course have the following characteristics. They


All readings should be read before class for full understanding of the subject material.

The required text for the course is:

We use this book because it is the least technical textbook that covers the material of the class. The 3rd edition has some improvements over the 2nd edition, and rearranges some of the material. You could probably get away with using a 2nd edition if you did not mind consulting a classmate’s textbook when necessary. Do not use the 1st edition. The book has a web site where you can download data sets and replication files here: Stock and Watson Student Resources.

There are two optional textbooks:

Students often ask me for a suggestion of what to read to get ready for 328, especially if it has been a while since they have taken 200. Gonick and Smith is the book I recommend. It reviews the material in 200, and previews some of the material in 328. It does so in an easily digestible format (cartoons), and it is a quick read (for a statistics book). It is also relatively cheap.

In 328, we use the statistical program Stata extensively. The Baum book covers a lot of the same material as Stock and Watson, though it has less detail and explanation. However, it integrates the material on regression with how to run any particular model in Stata. The TA lab sessions will also explain how to implement different models in Stata, but if you want an independent source of information on how to use Stata in context of 328, this is an excellent book.

There will be other readings available through links I will provide.

Lectures and Labs

The course will be run primarily as a lecture. Identical lectures will be presented at 1:35 p.m. and 3:00 p.m. on Tuesdays and Thursdays in 2111 JKB. You are welcome to attend either lecture. We will also have in-class learning exercises. In addition, I actively encourage questions and comments germane to our discussion. I urge--indeed, I expect--you to take advantage of the chance to talk with the Teaching Assistants and me during office hours.

Regrettably, a handful of students occasionally demonstrate insensitivity to other students and to instructors by disrupting classes unnecessarily. Arriving late for class, reading newspapers in class, packing up bags prior to the end of class, and cell phone use are all disruptive activities. Browsing the internet, checking email, and playing games on laptops are also inappropriate in class because you should be listening and participating.

Certainly, taking notes on a laptop is appropriate, but do not waste your time or mine by getting distracted by other activities on the web. Moreover, I will not tolerate incivility of one opinion to another. It is exciting and healthy to exchange a diversity of opinions, but in no case should anyone demean another because of his or her viewpoint. In a statistics class, many concepts are difficult to grasp and student understanding is not uniform across the class. If students ask questions that you feel others should already know, this should never be cause for frustration or otherwise being impatience. You might find yourself on the other end with a different concept. If you have any questions about what classroom civility entails, please contact me.

On Friday mornings, there will be six computer labs in 102 SWKT (first lab on the left in the FHSS Computer Center), 105 SWKT (second lab on the left), and 112 SWKT (fishbowl at the back). There are two labs each at 9 a.m., 10 a.m., and 11 a.m., and are assigned according to registration. These labs are led by the Teaching Assistants. You should attend your assigned lab each week.

In the labs you will learn how to do basic and advanced statistics in Stata. The Stock and Watson website has a helpful tutorial here as well: Stock and Watson Stata tutorial. You will also learn how to do statistics in a couple of other programs to increase flexibility and marketability for future work opportunities. Each week, the lab will cover the commands necessary to do the weekly assignments. The labs will also go over previous weekly assignments and sample exam problems.

During the second week of class, the FHSS Research Support Center will hold Stata workshops. Attending one of the workshops will give you a good base to begin your work with Stata this semester. The times and locations are:

(Each workshop is identical.) The Research Support Center is another resource available throughout the semester for any questions relating to Stata or statistical analysis or research.

You may find it useful to purchase your own copy of Stata. If you do not purchase your own copy, you need to plan ahead to use the computers in SWKT. Since some data sets we use have more than 1000 observations, you will need to purchase Stata/IC or Stata/SE.

Please arrive in the Computer Lab before class starts to sign in and have everything ready to go when class starts.

Academic Honesty and Plagiarism

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 [PlSc 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 Group Guidelines"

"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:

  1. You discuss concepts, approaches and methods that could be applied to a homework problem before either of you start your written solution. This process is encouraged. You are not required to make a written acknowledgment of this type of interaction.
  2. After working on a problem independently, you compare answers with another student, which confirms your solution. You must acknowledge that the other student's solution was used to check your own. No credit will be lost due to this comparison if the acknowledgment is made.
  3. After working on a problem independently, you compare answers with another student, which alerts you to an error in your own work. You must state at the end of the problem that you corrected your error on the basis of checking answers with the other student. No credit will be lost due to this comparison if the acknowledgment is made, and no direct copying of the correct solution is involved.
  4. You and another student work through a problem together, exchanging ideas as the solution progresses. Each of you must state at the end of the problem that you worked jointly. No credit will be lost due to this cooperation if the acknowledgment is made. [You must still write up your solutions individually, not jointly.]
  5. You copy all or part of a solution from a reference such as a textbook. You must cite the reference. Partial credit will be given, since there is some educational value in reading and understanding the solution. However, this practice is strongly discouraged, and should be used only when you are unable to solve the problem without assistance.
  6. You copy verbatim all or part of a solution from another student. This process is not considered academically dishonist if the acknowledgement is made. However, you will receive no credit for verbatim copying from another student as you have not made any intellectual contribution to the work you are both submitting for credit.

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:


Title IX of the Education Amendments of 1972 prohibits sex discrimination against any participant in an educational program or activity that receives federal funds. The act is intended to eliminate sex discrimination in education. Title IX covers discrimination in programs, admissions, activities, and student to student sexual harassment. BYU's policy against sexual harassment extends not only to employees of the university but to students as well. If you encounter unlawful sexual harassment or gender based discrimination, please talk to your professor; contact the Equal Employment Office at 422-5895 or 367-5689 (24 hours); or contact the Honor Code Office at 422-2847.

Brigham Young University is committed to providing a working and learning atmosphere which 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 (2170 WSC, 422-2767). Reasonable academic accommodations are reviewed for all students who have qualified documented disabilities. Services are coordinated with the student and instructor by the SSD office. 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. You should contact the Equal Employment Office at 422-5895, D-282 ASB.

Schedule (subject to change)

We will cover about a chapter a week. Here are slides from past lectures:

Other information:

Other readings:

Note: SW = Stock and Watson; AF = Agresti and Finlay





August 28

Introduction SW:1; AF:2.1-2.3



Probability SW:2.1-2.3; AF:3.1-3.4 Assignment 0 Due

September 4

Sampling Distribution SW:2.4-2.6  


Sampling Distribution AF:4.1-4.6

Assignment 1 Due


Statistics SW:3  


Experiments SW:13.1-13.2 Assignment 2 Due


Simple Regression SW:4.1-4.2  


Simple Regression SW:4.3-4.6 Assignment 3 Due


Simple Regression SW:5.1-5.3



Simple Regression SW:5.4-5.7

Assignment 4 Due

October 2

Multiple Regression SW:6.1-6.3  


Multiple Regression SW:6.4-6.8 Assignment 5 Due


Multiple Regression SW:7  


More Experiments SW:13.1-13.3

Assignment 6 Due
Midterm Distributed


No class: Work on Midterm  



Functional Forms SW:8.1 Midterm Due


Functional Forms SW:8.2  


Interactions SW:8.3 Assignment 7 Due


Interactions SW:8.4-8.5  

November 1

Validity SW:9 Assignment 8 Due


Panel Data SW:10.1-10.4  


Quasi-Experiments SW:10.5-10.7; 13.4.1 Assignment 9 Due


Binary Dependent Variables SW:11.1-11.2  


Binary Dependent Variables SW:11.3 Assignment 10 Due


No class: Friday Instruction    


No class: Thanksgiving  


Binary Dependent Variables SW:11.4  


Binary Dependent Variables SW:11.5 Assignment 11 Due

December 4

Instrumental Variables SW:12.1-12.3  


More Quasi-Experiments SW:12.4-12.6; 13.4.2-13.6

Assignment 12 Due


    Final Distributed


    Final Due


I often use video clips from popular culture (television programs, movies) to illustrate and emphasize the readings.


I consulted numerous syllabi in designing this course. Particularly helpful were syllabi by Mike Findley, Jeremy Pope, Michael Bailey, Adam Glynn, Simon Jackman, John Jackson, Andrew Martin, and James Stock.

Jay Goodliffe's home page

This page is http://goodliffe.byu.edu/328/syllabus.htm