| Lectures: | TTh 1:35 - 2:50 p.m. | B032 JFSB |
| or | TTh 3:00 - 4:15 p.m. | B032 JFSB |
| Computer Labs: | F 9:00 - 9:50 a.m. | 102 or 112 KMBL |
| F 10:00 - 10:50 a.m. | 102 or 112 KMBL | |
| F 11:00 - 11:50 a.m. | 102 or 112 KMBL | |
| F 12:00 - 12:50 p.m. | 102 or 112 KMBL |
| TA Office Hours: | |
| Monday: | 11:30a-1:00p, 2:00-4:00p |
| Tuesday: | 12:00n-1:30p, 5:00p-8:00p |
| Wednesday: | 11:00a-7:00p |
| Thursday: | 9:30a-1:00p |
| Friday: | 10:00a-12:00n |
| Teaching Assistants: | ||||
| Section 1: | Miranda Hatch | mirandahatch32@gmail.com | W 1-3, F 10-12 | 122 KMBL |
| Section 2: | Benjamin Mangus | benjaminmangus@gmail.com | W 3-4, Th 10-12 | 122 KMBL |
| Section 3: | Connor Kreutz | connorkreutz@gmail.com | M 11:30-1, Th 9:30-11 | 122 KMBL |
| Section 4: | Madeline Beck | beckmadelinec@gmail.com | M 3-4, W 6-7, Th 12-1 | 122 KMBL |
| Section 5: | Dani Hogan | dhogie@comcast.net | M 2-3, T 5-8 | 122 KMBL |
| Section 6: | Samantha Frazier | samanthafrazier4@gmail.com | W 11-1 | 122 KMBL |
| Section 7: | Amerins Tolman | amerins1@gmail.com | W 3-6 | 122 KMBL |
| Section 8: | Braydon Madson | braydonwade@gmail.com | T 12-1:30, W 4-7 | 122 KMBL |
In this course, you will learn the basics of using statistics to do data-driven research on political, economic, and social issues.
Statistical research in the social sciences requires several technical skills: an understanding of some basic probability and statistical theory, knowledge of how to apply that theory to different kinds of data and different kinds of issues, and basic computer skills necessary to obtain and analyze data.
Doing statistical research requires other skills as well: formulating a well-defined question, surveying previous research on the topic, generating original ideas and insights supported by evidence and careful arguments, and effectively communicating your analyses and results to others through written reports and creative data visualizations.
During the regular class meetings, we will cover some basic probability and statistical inference, and then use that foundation to understand the rationale for a variety of applied statistical methods. In the lab you will learn how to work with data sets and statistical software, and experience the process of doing empirical social science research.
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:
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 on Mondays 4:00-5:00 pm and Tuesdays 9:30-10:30 am. 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. (As noted below, if you see me in my office during the first two weeks of class, I will drop one of your quizzes.)
The TA office is located in 122 KMBL. 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):
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 | 40% |
Pre-class Quizzes | 5% |
Midterm Exam | 15% |
Final Exam | 35% |
Participation | 5% |
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 KMBL) and electronically on Learning Suite. If you have some sort of emergency, make sure you turn in your assignment in some format on time (email it to the instructor if you need to), after which you will turn in the other version(s) 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. Failure to turn in both versions will result in escalating penalties.
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. This is the time that the most students will have the assignment problems in the forefront of their memory, and thus, the best time to discuss it. Since the problem will be related to the previous week's material, discussing the assignment along with that day's material will interleave the content, which promotes retention.
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.
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.)
Except where explicitly directed otherwise, you may work together on these assignments in groups of two or at most three, but you must write up your answers separately. (Three means you and two other students.) 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, my name (Professor Goodliffe), 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
Professor Goodliffe
Your section (1, 2, 3, 4, 5, 6, 7, or 8)
Lab instructor: Smith, Jones, 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.
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 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 2 lowest-scoring quizzes. You will have the opportunity to drop 3 more quizzes: 1 quiz if you visit me in my office during the first two weeks of class, and 1 quiz each 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.
Participation consists of attending class and, in particular, attending the weekly computer labs on Friday, and not disrupting those labs.
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:
To use #2, you must complete at least 10 of the 11 weekly assignments (Assignment 0 does not count), at least 23 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 2018-2019 Undergraduate Catalog, which guides how I grade and determine workload:
| A | Excellent |
| B | Good |
| C | Satisfactory |
| D | Minimum Passing |
| E | Failure |
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 one of the least technical textbooks that covers the material of the class. The 3rd edition has some improvements over the 2nd edition, and rearranges some of the material. The 3rd updated edition has updated problems and data. You could probably get by with using a 3rd edition if you did not mind consulting a classmate's textbook when necessary. There is an international edition, which has different questions and problems at the end of each chapter. If you used this, you would need to check every problem against the 3rd updated 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 Acock book covers much of the same material as Stock and Watson, though it emphasizes Stata commands over statistical cooncepts. However, it integrates the material 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 Learning Suite.
Identical classes will be presented at 1:35 p.m. and 3:00 p.m. on Tuesdays and Thursdays in B032 JFSB. We will also have in-class learning exercises. I will post slides before each class. 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.
Research on student learning has found that writing notes by hand is more effective than taking notes on a laptop. This is because students taking notes by hand usually write down concepts and principles in their own words, whereas students taking notes by laptop attempt to type all of the words that are spoken. In other words, students who take notes by hand create their own knowledge and students who use a laptop merely write down someone else's words. Taking notes on a laptop is acceptable, but do not waste your time or mine by getting distracted by other activities on the web.
Be civil to one 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 eight computer labs in 102 KMBL (first lab on the left in the FHSS Computer Center), and 112 KMBL (fishbowl at the back). There are labs at 9 a.m., 10 a.m., 11 a.m., and 12 noon, and are assigned according to registration. These labs are led by the Teaching Assistants. You should attend your assigned lab each week. (We may change the assignments so that a similar number of students are in each lab.) 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 KMBL.
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 KMBL computer labs, and available to install on your own computer for free.
Early in the semester, the FHSS Research Support Center will hold Stata workshops. Attending the workshops will give you a good base to begin your work with Stata this semester. The times and locations for the Stata workshops are:
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:
I have placed an examples of how to write up homework, including examples of citing work with others, on Learning Suite here (blank example) and here (filled-in examples).
In accordance with Title IX of the Education Amendments of 1972, Brigham Young University prohibits unlawful sex discrimination against any participant in its education programs or activities. The university 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 all university employees in a teaching, managerial, or supervisory role to report all incidents of Sexual Misconduct that come to their attention in any way, including but not limited to face-to-face conversations, a written class assignment or paper, class discussion, email, text, or social media post. Incidents of Sexual Misconduct should be reported to the Title IX Coordinator at t9coordinator@byu.edu or (801) 422-8692. Reports may also be submitted through EthicsPoint at https://titleix.byu.edu/report or 1-888-238-1062 (24-hours a day).
BYU offers confidential resources for those affected by Sexual Misconduct, including the university's Victim Advocate, as well as a number of non-confidential resources and services that may be helpful. Additional information about Title IX, the university's Sexual Misconduct Policy, reporting requirements, and resources can be found at http://titleix.byu.edu or by contacting the university's Title IX Coordinator.
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.
We will cover about a chapter a week.
Note: SW = Stock and Watson; AF = Agresti & Finlay; GS = Gonick and Smith; Ac = Acock; B = Bailey
Date |
Topic |
Readings |
Optional |
Assignments |
|---|---|---|---|---|
September 4 |
Describe/Visualize data | SW:1; AF:3.1-3.5; NYT; Quartz | GS:1-2 |
|
6 |
Probability | SW:2.1-2.3; AF:2.1-2.3; Regulation | GS:3-4 | Assignment 0 due |
7 |
Lab 1 | Ac:1,4,5,6 | ||
11 |
Normal distribution | SW:2.4-2.6 | GS:5 | |
13 |
Sampling distribution | AF:4.1-4.6; Khan 1; Khan 2 | GS:6 | Assignment 1 due |
14 |
Lab 2 | Ac:2 | ||
18 |
Hypothesis tests | SW:3.1-3.2 | GS:8 | |
20 |
Confidence intervals | SW:3.3; Guardian | GS:7 | Assignment 2 due |
21 |
Lab 3 | Ac:3 | ||
25 |
Comparing two groups | SW:3.4-3.6; Cambridge | GS:9-10 | |
27 |
Simple regression | SW:3.7, 4.1-4.3 | GS:11 | Assignment 3 due |
28 |
Lab 4 | Ac:7 | ||
October 2 |
Simple regression | SW:4.4-4.6, 5.1-5.2 | ||
4 |
Multiple regression | SW:6.1-6.4; Vox | Assignment 4 due |
|
5 |
Lab 5 | Ac:8 | ||
9 |
Multicollinearity | SW:6.5-6.8, 7.1-7.4, 5.3; EV; B:Dummy | ||
11 |
Presenting results | SW:7.5-7.7, 13.1-13.3 | Assignment 5 due | |
12 |
Lab 6 | Ac:10 | ||
16 |
Heteroskedasticity | SW:5.4-5.7, 7.2.4 | ||
18 |
Review | Assignment 6 due |
||
19 |
Lab: Midterm Preparation | Midterm distributed | ||
23 |
No class: work on midterm |
|
||
25 |
Diagnostics, polynomials | SW:8.1-8.2; B:Polynomials | Midterm due | |
26 |
Lab 7 | |||
30 |
Logarithms | SW:8.2; B:Logs | ||
November 1 |
Interactions | SW:8.3-8.5; B:Interactions | Assignment 7 due | |
2 |
Lab 8 | |||
6 |
Validity | SW:9, 13.2 | ||
8 |
Panel data | SW:10.1-10.2 | Assignment 8 due | |
9 |
Lab 9 | |||
13 |
Fixed Effects | SW:10.3-10.4; B:Fixed effects | ||
15 |
Quasi-experiments | SW:10.5-10.7, 13.4.1 | Assignment 9 due | |
16 |
Lab 10 | |||
20 |
Friday Instruction: Review Lab | |||
27 |
LPM, Probit | SW:11.1-11.2 | ||
29 |
Logit, fit | SW:11.3-11.5 | Assignment 10 due | |
30 |
Lab 11 | Ac:11 | ||
December 4 |
Instrumental variables | SW:12.1-12.3 | 6 |
More quasi-experiments | SW:12.4-12.6, 13.4.2-13.6 | Assignment 11 due |
7 |
Lab 12 | Final distributed | ||
11 |
No class: work on final | |||
13 |
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 and used parts of numerous syllabi in designing this course. Particularly helpful were syllabi by Mike Barber, 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