Political Science 328
Statistical Analysis

Course Syllabus
Winter 2016

Lectures: TTh 1:35 - 2:50 p.m. 202 MSRB
or TTh 3:00 - 4:15 p.m. 202 MSRB
Computer Labs: F 9:00 - 9:50 a.m. 102 or 112 SWKT
  F 10:00 - 10:50 a.m. 102 or 112 SWKT
  F 11:00 - 11:50 a.m. 102 or 112 SWKT

Instructor: Jay Goodliffe
Office: 734 SWKT
Office Hours: MW 1:30-2:30 p.m., and by appointment
Phone: 801.422.9136
e-mail: goodliffe@byu.edu

Teaching Assistants:
Section 1: Naomi Dorsey dorseynaomi@gmail.com M1, W1, W2: 122 SWKT
Section 2: Brodie Wray wray.brodie@gmail.com T5, T6, W6: 122 SWKT
Section 3: Amy Cloud amayacloud@gmail.com M8a, T12, W8a: 122 SWKT
Section 4: Becca Dudley rebecca.ellen.dudley@gmail.com M10, W5, W6, F2: 122 SWKT
Section 5: Jacob Nielsen jacobkurtnielsen@gmail.com T7, W7, W8p, F1: 122 SWKT
Section 6: Faith Conlin faithconlin@yahoo.com T10, W7, W8p: 122 SWKT

TA Office Hours:
Monday: 8a, 10, 11
Tuesday: 10, 12, 5, 6, 7
Wednesday: 8a, 1, 2, 5, 6, 7, 8p
Friday: 1, 2


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


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.

Office Hours

I will hold office hours on Mondays and Wednesdays 1:30-2:30 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.

The TA office is located in 122 SWKT. The names and contact information for each TA are listed above. Here are some guidelines for working with the TAs during office hours (many suggested by students in past course evaluations):

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 use numerical tools to explain the world in quantitative terms, interpret numerical data, and 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 fulfills University Core Learning Outcomes:


Political Science 200 is a prerequisite for this course. Political Science 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 difficult to succeed in this course. If you have not taken Political Science 200, take this course after you have. (You may not enroll concurrently in Political Science 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


Pre-class Quizzes


Midterm Exam


Final Exam


All weekly assignments are due on Thursdays at 1:30 p.m. both on paper in the Political Science Assignment Drop Box (located outside the entrance to the department office at 745 SWKT) and electronically on the Learning Suite. If you have some sort of emergency, make sure you turn in your assignment electronically (email it to the instructor if you need to), after which you will turn in a hard copy as soon as possible. Paper versions of the assignments must be typed and separated into four different packets, as directed on the assignment directions. Electronic versions of the assignment are also uploaded using four separate documents.

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 one lowest assignment for the semester. I strongly suggest that you do not plan to drop an assignment. If you have a bad week, you would be better off turning in that assignment partially completed than dropping that assignment. You will need to know that material for the exams, so you will need to learn the material in the assignment later if you miss. Every semester, students who have already dropped an assignment early in the semester for convenience ask to drop another assignment later in the semester because of a real problem out of their control, such as illness, car accident, funeral, computer crash, fire alarm, etc. My answer is that I will drop the assignment for the real problem, but not drop the assignment dropped earlier for convenience.

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 Learning Suite after class on Thursday. (We will record scores on assignments, pre-class quizzes, 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. The reason I limit group size is that students have a greater opportunity to learn and understand the material when they work in smaller groups. (Conversely, it is easier to mistakenly think you know the material when you work in larger groups.)

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. As a regular practice, on each problem, state with whom you worked. If you worked alone, state that you worked alone.

Each of the assignments will be divided into four parts. For the paper version, you will need to staple each part separately one from another. 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, Poli 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, 4, 5, or 6)
     Lab instructor: Cloud, Dorsey, etc.
     Assignment #
     Part #
     Answers begin here...

At the end of each assignment, there is a survey that students take (in Qualtrics) reporting the number of hours spent on each part of the assignment. You are also awarded points for completing this survey.

Pre-class Quizzes

As much as possible, we will work on solving problems together in class. This requires students to read the material before class. To encourage students to read before class, there will be a quiz before each class on the reading material from Stock & Watson and Agresti & Finlay covered in class. The quiz will usually consist of 5 multiple-choice questions on Learning Suite. This is an open book quiz, but you may not consult with anyone else on the quiz. You must finish the quiz by 1:30 p.m. I will drop the 3 lowest-scoring quizzes. You will have the opportunity to drop 2 more quizzes if you turn in the course evaluations (from BYU and from me) at the end of the semester.


There is a midterm and final exam. For both exams, there is a take-home component and a Testing Center component. The take-home component will require you to solve problems similar to case studies in the weekly assignments, using any personal notes and public resources. The take-home component is taken home for a week, and is in place of the weekly assignment. (There is no class during the midterm or final exam.) The Testing Center component will require you to interpret statistical analysis generated by others and explain statistical concepts, using no notes. You are not allowed to consult with anyone on these exams.

Research Project

Unlike previous semesters of Political Science 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.

Unlike many other classes at BYU, the points you receive do not correspond to percentage of the material learned, or to particular letter grades. In other words, a 75 (out of 100) does not mean that you understand 75% of the material, nor does it mean you have a C. It means you scored higher than anyone that received a 74 or lower, and scored lower than anyone that received a 76 or higher.

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 11 weekly assignments (Assignment 0 does not count), at least 22 of the 25 pre-class quizzes, and the midterm exam. (Note: 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 2015-2016 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

At the end of the course, I ask students to fill out a survey about their experiences in the class. One of the questions I ask is what advice they would give students who are starting the course. Here are the most frequent responses:


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. (You can also use the updated 3rd edition, published July 2014.) 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 three 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.

Students also ask me for a conceptual alternative to Stock and Watson. The Angrist and Pischke book covers much of the material of the class, with a stronger emphasis on causal inference.

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 from Agresti and Finlay available through Learning Suite.

Lectures and Labs

Identical classes will be presented at 1:35 p.m. and 3:00 p.m. on Tuesdays and Thursdays in 202 MSRB. We will also have in-class learning exercises. I will post slides before each class so that you may take notes on your copy of the slides. 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 impatient. 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), and 112 SWKT (fishbowl at the back). There are labs 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. Please arrive in the Computer Lab before class starts to sign in and have everything ready to go when class starts.

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. 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.

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 1200 observations, you will need to purchase Stata/IC or Stata/SE.

Although our primary statistical program is Stata, you will also learn how to do basic statistics and regression in R to increase flexibility and marketability for future work opportunities. We will use the GUI front-end RStudio. Both of these are available in the SWKT computer labs, and available to install on your own computer for free.

Early in the semester, the FHSS Research Support Center will hold Stata and R workshops. Attending the workshops will give you a good base to begin your work with Stata (and R) this semester. The times and locations for the Stata workshops are:

(Each workshop is identical.) The times and locations of the R workshops 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.

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 [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 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 dishonest 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:

I have placed an example of how to write up homework, including examples of citing work with others, on Learning Suite.


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.

Note: SW = Stock and Watson; AF = Agresti and Finlay; AP = Angrist and Pischke; GS = Gonick and Smith; B = Baum






January 5

Describe/Visualize data SW:1; AF:3.1-3.5 GS:1-2; AP:Intro.



Probability SW:2.1-2.3; AF:2.1-2.3 GS:3-4 Assignment 0 due


Lab 1   B:1  


Normal distribution SW:2.4-2.6 GS:5  


Sampling distribution AF:4.1-4.6 GS:6; AP:33-46

Assignment 1 due


Lab 2   B:2  


Hypothesis tests SW:3.1-3.2 GS:8  


Confidence intervals SW:3.3 GS:7 Assignment 2 due


Lab 3   B:Appendix A  


Comparing two groups SW:3.4-3.6 GS:9-10; AP:1  


Simple regression SW:3.7, 4.1-4.3 GS:11 Assignment 3 due


Lab 4      

February 2

Simple regression SW:4.4-4.6, 5.1-5.2 AP:2  


Multiple regression SW:6.1-6.4  

Assignment 4 due


Lab 5   B:4  


Multiple regression SW:6.5-6.8, 7.1-7.4, 5.3    


Presenting results SW:7.5-7.7, 13.1-13.3   Assignment 5 due


Lab 6   B:6  


No class: Monday instruction      


Regression extensions SW:5.4-5.7, 7.2.4  

Assignment 6 due
Midterm distributed


No lab: work on midterm      


No class: work on midterm    



Diagnostics, polynomials SW:8.1-8.2   Midterm due


Lab 7   B:5  

March 1

Logarithms SW:8.2    


Interactions SW:8.3-8.5   Assignment 7 due


Lab 8   B:7  


Validity SW:9, 13.2    


Panel data SW:10.1-10.2   Assignment 8 due


Lab 9   B:3  


Fixed Effects SW:10.3-10.4    


Quasi-experiments SW:10.5-10.7, 13.4.1 AP:5 Assignment 9 due


Lab 10   B:9  


LPM, Probit SW:11.1-11.2    


Logit, fit SW:11.3-11.5   Assignment 10 due


Lab 11   B:10  


Instrumental variables SW:12.1-12.3 AP:3  


Two stage least squares SW:12.4-12.6    

April 1

Lab 12   B:8  


More quasi-experiments SW:13.4.2-13.6 AP:6

Assignment 11 due
Final distributed


No class: work on final      


No lab: work on final      


How to use 328   GS:12 Final due


I sometimes 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, Josh Gubler, 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