Political Science 306
Advanced Statistical Analysis

Course Syllabus
Fall 2015
MW 3:00 - 4:15 p.m. in 121 MSRB

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


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

Learning Outcomes

This course explores advanced topics in econometrics, building on basic linear regression learned in Political Science 328. 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:


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 after class 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.


Political Science 328 (or an equivalent class, such as Economics 388) is a prerequisite for this course. That course covered basic statistics, ordinary least squares and its pathologies, fixed effects models, and logit/probit models. If you have not taken Poli 328, you need to speak to me before proceeding. Students who mastered the material in their prerequisite course will be at an advantage.


We will emphasize application and interpretation over theory. Thus, in addition to the textbooks, we will read articles that apply these methods to problems in political science and public policy.

The course will be run primarily as a lecture. 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 me during office hours.


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


Research Project


All assignments are due at the beginning of class, usually on Wednesdays. 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.

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 Wednesday. (We 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.


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 exams are taken home for a week, and are in place of the weekly assignment. (There is no class during a take-home exam.) The final exam will cover material for the whole semester.

Research Project

Students will write and present a paper on a topic of their choosing. The project will allow you the opportunity to apply the skills that we will develop in this class to actual data and problems. You may pursue any topic of your choice, subject to instructor approval. (One requirement is that you have the necessary data.) As we are spending the most time on multilevel/hierarchical models, a research question that uses such data would be a good choice. There are a number of deadlines that must be met, noted on the course schedule.

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 statistical model. In their brief edition, Stock and Watson have a helpful chapter on "Conducting a Regression Study."


Date Due



September 16

part of Assignment 2

Data Summary

September 30

part of Assignment 4

ORCA proposal

October 28

part of Assignment 7

Presentation April 7-9



April 7



Turn in a 1-2 page, double-spaced proposal (standard font and margins) outlining the research question you plan to address, explains a potential causal connection linking an independent and dependent variable, suggests which econometric model(s) will be used, offers at least 4 relevant citations, and discusses possible data sources to be used.

Data Summary

Turn in a one-page, double-spaced document (standard font and margins) that offers details about the data set that you have obtained. The summary should include summary statistics and any relevant figures that help describe the data.

ORCA proposal

Submit a proposal of your research project to ORCA. Details are found here. Attend one of the proposal workshops: September 30 (11a or 3p) or October 1 (11a or 3p) in the Varsity Theater.


All students will present their research during the last week of class. The presentation's technical level should be geared toward a generic public servant: You should avoid tables and technical details, and you will have to explain what your statistical results mean. There will be a strict time limit, and you should be prepared to answer questions from the class and instructor. Further suggestions on presentations generally can be found here.

Final Paper

The paper's technical level may be higher than the presentation. However, you should still explain what your statistical results mean in layperson's terms. You should provide an abstract. In grading the paper, I will consider how well you have used material from the course, how well you have used statistical analysis to test your hypotheses, if the analysis is actually correct (numerical accuracy and correct interpretation), how well you use charts and graphs, logic and organization of the paper, and the usual grammatical and spelling concerns. Follow the examples of published papers we read in class. As a statistical analyst, it is important that you are aware of the limitations of your research. Under what circumstances do your results hold? Likewise, which circumstances would make them invalid? If you are unable to conduct the ideal analysis (perhaps due to resource constraints), explain what the proper approach would be. If you were able to use this superior approach, how would the results likely differ from the results you have? The papers may be picked up in the Political Science office (745 SWKT) after they are graded.

How to Succeed in this Course

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 will award the higher of two grades:

  1. your overall point total
  2. your point total using only the final exam and research project

To use #2, you must complete the 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 or research project.

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 9 hours a week to receive an 'A' (= "excellence") in a 3 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

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:


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.


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. (The Stata Press site sometimes has better prices than Amazon.)

The first book (Stock and Watson) was used in Poli 328. We use this book because it is the least technical textbook that covers the material of the classes. 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 could also use the updated 3rd edition. 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.

The second book (Rabe-Hesketh and Skrondal) has two volumes. We cover material mostly in the first volume, but you are welcome to buy the second volume. The first chapter of Rabe-Hesketh and Skrondal has a nice review of linear regression.

In the labs you will learn how to do various 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 other programs to increase flexibility and marketability for future work opportunities.

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.


The schedule is subject to more change than usual to follow the abilities and interests of the class.

Note: SW=Stock and Watson; RS=Rabe-Hesketh and Skrondal





August 31

Instrumental Variables SW:12.1-12.3


September 2

Two Stage Least Squares SW:12.4-12.6
Berry, Fording, and Hanson

Assignment 0 due


No class: Holiday  



Quasi-experiments SW:13.4.2-13.7
Feyrer and Sacerdote

Assignment 1 due


Ordered Logit/Probit SW:A11.3; Baum:10.1-10.2


Multinomial Logit Cameron and Trivedi:15.1-15.5
Campbell, Green, and Monson

Assignment 2 due
Project Proposal


Tobit Baum:10.3
Goodliffe and Hawkins


Sample Selection Baum:10.4

Assignment 3 due


Time Series SW:14.1-14.5



Breaks SW:14.6-14.8
Lewis-Beck and Alford

Assignment 4 due
Project Data Summary

October 5

Dynamics SW:15, DeBoef and Keele
Goodliffe and Hawkins



Cointegration SW:16

Assignment 5 due
Midterm distributed


No class: work on midterm  



Variance Components RS:2
Garner and Raudenbush

Midterm due


Random Intercepts RS:3
Bosshardt and Watts


Random Intercepts RS:3

Assignment 6 due


Random Coefficients RS:4
Goodliffe and Hawkins


Longitudinal/Growth Curve Models RS:5,7
Curran, Stice, and Chassin

Assignment 7 due
ORCA proposal

November 2

Hierarchical Logistic RS:10
Bartfeld and Dunifon


Nested and Crossed Random Effects RS:8-9
Brown and Goodliffe

Assignment 8 due


Count Models Cameron and Trivedi:17.1-17.3.3
Grant and Rutner


Discrete-time Survival Allison
Goodliffe and Hawkins

Assignment 9 due


Nonparametric Survival Cleves et al.:1,8


Cox Survival Box-Steffensmeier and Jones

Assignment 10 due
Final exam distributed


No class: work on final  



No class: holiday  



How to write and present a paper SW, brief edition:10

Final due

December 2

No class: work on papers Wooldridge:19  


Class Presentations  

Paper due


Class Presentations    

Jay Goodliffe's home page

This page is http://goodliffe.byu.edu/306/syllabus.htm