Professor Florian M. Hollenbach
Email: email@example.com; Web: fhollenbach.org
Office: 2061 Allen Building; Phone: 979-845-5021
Office Hours: Monday, Wednesday, & Friday: 10:40am - 11:45am or by appointment
Office: 2050 Allen Building
Office Hours: Tuesday 1:30pm - 2:30 pm & Thursday 11:00am & 12:00pm (noon)
Class Meeting Time:
Monday, Wednesday, Friday:
- Section 905: 8:35 am – 9:25 am
- Section 906: 9:45 - 10:35am
Class Location: Bush Academic Building West (ALLN) __ 2003__ Unless otherwise noted or announced) Note this is a change from the original class location
All class materials, syllabus, notes, assignments will be posted on the class website: https://fhollenbach.github.io/Polisci209_2018
All assignments are to be submitted via eCampus, which is where I will also post your grades.
“I keep saying that the sexy job in the next 10 years will be statisticians, And I’m not kidding.”
Hal Varian, chief economist at Google
“Without data you’re just another person with an opinion.”
W. Edwards Deming
Data and data analysis are becoming more and more important for us as citizens in the modern nation state, the modern work place, and as consumers of increasingly complex information. At the same time, the understanding of statistical fundamentals is as pertinent as ever to read any political science research. This class serves two main purposes. First, it will help you understand the basic statistics that are necessary to read modern political science research. Second, you will gain an understanding of basic methods of data analysis and the underlying concepts of probability. We will also cover some introductory programming, so that you will be able to write code for basic statistical functions and plots in R.
At the end of the semester, after completing this course, students are expected to:
Understand the concept of causality and experimental designs
- Be able to do simple programming in R, such as:
- merge and subset data files
- plot and interpret histograms, scatterplots, boxplots
- run simple statistical models
- present data using graphics and descriptive statistics in a clear and informative manner
- Explain and understand simple descriptive, univariate, and bivariate
statistical concepts, such as:
- randomization – mean – (co)variance, correlation – measurement – Central Limit Theorem – bivariate linear regression
- hypothesis tests
COURSE STRUCTURE & REQUIREMENTS:
The class will meet three times a week on Monday, Wednesday, and Friday. Classes will not simply be lecture based. We will cover programming, examples, and do in class exercises. This class will cover a variety of (complicated) concepts. Generally concepts will first be covered in the readings and we will go over specific applications and your questions on these concepts in class. It is therefore important for you to do you do the required reading and exercises listed under each date’s header before the respective class period. For most weeks, the readings & topics covered can be quite technical and challenging, which means it is even more important that you try to understand the material before coming to class. If you do not understand part of the readings, it is important to raise questions in class. That is the whole purpose of class time. I guarantee you will not be the only one that has trouble with the material and by asking questions, you are providing a service to your classmates. There will be regular homework and practice assignments. The assignments are for you to deepen your understanding of the material and study for the exam. Some of the assignments will be quite hard. It is, however, important for your own progress that you at least attempt to solve each problem on your own first, before seeking help. If you are stuck, I encourage you to seek help from your classmates first, then the TA or myself.
You are expected to do all readings prior to class, participate in class discussions, submit all assignments on time, and take quizzes and the final exam as scheduled.
GRADING & RESPONSIBILITIES:
Your grade will be based on one final exam (18%) at the end of the semester, two mid-semester quizzes (8% combined), class attendance (10%), participation, homework exercises (30% combined), and two longer writing assignments (34% combined). All assignments are to be submitted via eCampus before class on the day under which they are listed on the syllabus.
I expect you to do the assigned readings for each class before the lecture, participate in class discussions, and come prepared with questions. Specifically, you will be graded on:
- homework assignments/SWIRL review exercises: 30% of class grade
- SWIRL assignments will be graded on pass/fail. Each SWIRL exercise is worth one point. You will receive a pass for the respective exercise as long as you attempt all questions. As proof, you will save a screenshot of the log at the end and submit it to eCampus. Your Swirl grade will be the percentage of Swirl exercise for which you received a pass. Other homework assignment will be graded on the normal scale and receiving 100% will be worth four points.
- class attendance/participation: 10% of class grade
- Attendance will be marked at the beginning of each class. You are allowed to have two free unexcused absences. After two absences, I will deduct one point from the 10 possible attendance points for each unexcused absence. Excused absences are not deducted from your grade. I will decide what counts as excused on a case-by-case basis, but in general absences will only be excused for good reasons. You must contact me before missing class. If you have more than 15 unexcused absences, you will receive an F in the course. Similarly, while unexpected events can cause tardiness, if you repeatedly arrive late to class, I reserve the right to mark late arrivals as absences. Should you arrive to class late, be sure to let me know after class so that I can mark you as present.
- I expect all students to participate in class discussion, ask questions, listen to their fellow students, and be attentive. If you repeatedly fail to pay attention (e.g. fall asleep or play on your cell phone), I may deduct points from your participation/attendance grade.
- If you do have health problems that do not allow you to perform well in class, please come talk to me ASAP. I am willing to work with you, but this is only possible if you come talk to me early enough.
- Two writing assignment: 34% of class grade (17% each)
- The writing assignments in this class will be memoranda and are supposed to prepare you for real world work assignments. Each paper will be required to be 1000 - 1500 words. They will involve work with data in R. The writing assigments will be structured with the requirements of the modern work place in mind and you will receive detailed instructions. For both assignment you will provide a first draft to a fellow student, who will provide comments (via eCampus). You will incorporate the comments and will then be graded and receive additional comments by us on the final version. To pass this course, you must pass the writing components. Your comments to your fellow students will be graded as a homework exercise. All versions of the written assignments (first & final draft) are to be submitted via Turnitin on eCampus prior to class on the day they are due. Peer-review feedback will also be submitted on eCampus.
- Two in-class quizzes: 8% of class grade (4% each)
- We will have two short quizzes during the semester to make sure that everyone is keeping up and to give you some insight in how the final exam might look. Each quiz will be announced at least one week prior and will take about 10-15 minutes of class time.
- Final Exam (cumulative): 18% of class grade
- The final exam will cover the material from the whole class and will be worth 18% of your class grade. The exam focusses on all of the material covered in class, including the readings, lectures, and exercises. The assigned exercises should serve as excellent preparation for the exams.
The grading scale (in %) used in this class for all written assignments, exams, and the overall class grade will be the following:
- A= 89.5
- B= 79.5–<89.5
- C= 69.5–<79.5
- D= 59.5–<69.5
Given that this course is an official writing course, you must pass the writing part of the class, i.e., the written assignments, to pass the class. As noted on the website of the Texas A&M Writing Center: “What happens if I don’t pass the W or C portion of a W or C course? If you complete a course with a passing grade but have not passed the W [..] portion of the course, you will not get the graduation credit for that W or C course.” (Link) This is not negotiable.
The University Writing Center (UWC), located in 1.214 Sterling C. Evans Library and 205 West Campus Library, offers one-on-one consultations to writers. UWC consultations are highly recommended but are not required. Help is available with brainstorming, researching, drafting, documenting, revising, and more; no concern is too large or too small. UWC consultants will also help you improve your proofreading and editing skills. If you visit the UWC, take a copy of your writing assignment, a hard copy of your draft or any notes you may have, as well as any material you need help with. To find out more about UWC services or to schedule an appointment, call 458-1455, visit the web page at writingcenter.tamu.edu, or stop by in person.
All students should follow the highest standards of academic integrity. Cheating or plagiarism will not be tolerated in any way. If you are unsure what entails plagiarism, come talk to me. For more info, see: http://student-rules.tamu.edu/aggiecode & http://aggiehonor.tamu.edu. “An Aggie does not lie, cheat or steal, or tolerate those who do.” Your written assignments are to be submitted via Turnitin, which makes the detection of plagiarism and cheating very easy. Any cases of cheating or plagiarism will be submitted to the academic honor council, no exceptions.
Regarding group work: Unless explicitly otherwise specified, your homework and assignments are not to be done in groups and should be done alone. If you get stuck on a problem, you can discuss it in general terms with your fellow students, however, all solutions ought to be based your own work. Before asking for help from your fellow students, the TA, or myself, make sure you at least attempt to solve the problem yourself, otherwise you are only hindering your own learning.
READINGS & SOFTWARE:
We will primarily use Kosuke Imai’s “Quantitative Social Science”, which is available in the Texas A&M bookstore. You will need the hard copy of the book, otherwise it will be nearly impossible to pass the class. The book is comparatively affordable. If you truly can not afford to buy the book, come talk to me.
Required book: Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press. Princeton, NJ. ISBN: 978-0-691-16703-9.
You should have the book within the first week of class.
For part of this class we will be working on the computer with statistical software. We will use the statistical programming language R. R is available for download here:. I would recommend you download R-Studio, which is a software (a set of integrated tools) that makes the use of R much easier. You can download R-Studio here:. Both R and R-Studio are free. I would encourage you to install R-studio and play around with it for a bit.
You will also need a pocket calculator. You can buy a cheap one for at Walmart for about $3. Graphing calculators will not be allowed on the exams, so if you have any questions, please ask. Here is an example from Amazon for a calculator you could use. You should have your calculator by the Friday September 1st.
CLASSROOM BEHAVIOR, PARTICIPATION, & ELECTRONIC DEVICES:
We will usually meet three times a week during the semester. You can expect me to be prepared, give lecture, and answer questions. As outlined above, when you come to class, I expect you to be prepared as well and have the reading done before class. Remember, class is a resource to you. The exam and quizzes will be based on all lectures, readings, homework, and the discussions in class. Thus, only doing the required readings or only attending class will not be sufficient.
I strongly encourage you to not use a laptop in class, unless we are working together in R. Laptops have been shown to be a distraction not only to the students using them but also fellow class mates. A recent study has found that not having laptops in class can have a similar effect as hiring a SAT tutor
If you think you have good reasons for why you need to use a computer, you may do so, but I ask those with laptops to sit in the back of the classroom.
In addition, please make sure your cell phones are on silent mode and refrain from using them during class time. If you are repeatedly on your phone, I may deduct points from your participation/attendance grade.
EXAM ABSENCES & LATE POLICY:
Make-up exam/quizzes will be permitted only in the case of university-excused absences. To be eligible for a make-up exam/quizzes, you will have to present original written documentation of legitimate circumstances that prevented you from taking the exam/quiz on time. Except in the case of observance of a religious holiday, to be excused, the student must notify his or her instructor in writing (acknowledged e-mail message is acceptable) prior to the date of absence. In cases where advance notification is not feasible (e.g. accident or emergency) the student must provide notification by the end of the second working day after the absence. This notification should include an explanation of why the notice could not be sent prior to the class. Accommodations sought for absences due to the observance of a religious holiday can be sought either prior or after the absence, but not later than two working days after the absence. Legitimate circumstances include religious holidays, illness (verified by a doctor), serious family emergencies and participation in group activities sponsored by the University, etc. See http://student-rules.tamu.edu/rule07 for additional information. Please note that I do not accept Xeroxed copies of medical excuses from students.
Unexcused absences from either exam will result in a score of 0 for the exam. Unexcused late work will be penalized by a 7.5 percentage point deduction for each day your work is late. For example, if you hand in the a writing assignment on the same day it is due, but after the stated deadline, your maximum score will be 92.5%. If you hand in your assignment more than 24hrs late, your maximum score will be 85%, after 48hrs it would be 77.5%, and so on. Late work will be excused only in the case of university-excused absences. Only under extreme circumstance will I make exceptions to these rules.
Students that want to appeal a grade received on an exam or assignment must submit a regrading request in written form (e.g., email). This request has to be turned in within five working days after the graded exams or assignments are returned to the class. The written statement must explain exactly why the student believes the current grade is incorrect. I will then regrade the entire assignment or exam extra carefully. NOTE, as a consequence your grade may go up or down.
The best place to ask questions is in the classroom. If your question is not related to class material or relevant to other students, we can discuss it after class. I encourage you to visit my office hours to discuss any difficulties with the readings or homeworks. Again, however, you should at least attempt to solve the problem on your own first.
You can expect me to reply to emails within 24 hours during the work week. I will not reply to emails on the weekend, except for urgent matters. As with all business related correspondence, please include an appropriate salutation, identify yourself, and write in complete sentences.
All discussions will remain confidential. University policy is in accordance with the Americans with Disabilities Act Policy Statement. The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, currently located in the Disability Services building at the Student Services at White Creek complex on west campus or call 979-845-1637. For additional information, visit http://disability.tamu.edu.
Reasonable accommodations will be made for all students with disabilities, but it is the student’s responsibility to inform the instructor early in the term. Do not wait until just before an exam to decide you want to inform the instructor of a learning disability; any accommodations for disabilities must be arranged well in advance.
The Department of Political Science supports the Texas A&M University commitment to diversity, and welcomes individuals from any racial, ethnic, religious, age, gender, sexual orientation, class, disability, and nationality. (See http://diversity.tamu.edu/. In the spirit of this vital commitment, in this course each voice in the classroom has something of value to contribute to all discussions.
Everyone is expected to respect the different experiences, beliefs and values expressed by fellow students and the instructor, and will engage in reasoned discussion that refrains from derogatory comments about other people, cultures, groups, or viewpoints.
Changes to Syllabus
I reserve the right to update/modify/clarify the syllabus with advance notification.
Monday, August 27th: What are we doing in this class?
- Please fill out survey prior to Sunday night!
- Read Syllabus
- What is the most surprising thing about you?
- Buy text book
- Buy calculator
- Slides Week 1
Wednesday, August 29th: Installing and Introduction to R
- Read & Work through Chapter 1, pages 1-30
- Data files used in Chapter 1
Friday, August 31st: FASB PANEL – Meeting in 1015
- Panel with Polisci Alumni
Monday, September 3rd: More introduction to R & Getting Everyone up to Speed
Wednesday, September 5th: Introduction to causality
- Read & Work through QSS Chapter 2.1-2.4
- SWIRL Exercises INTRO1 & INTRO2
- Slides Week 2
Friday, September 7th: In Class Exercise on Causality
- Download data for in class exercise here
- Homework: SWIRL Exercise CAUSALITY 1
Monday, September 10th: Continue Exercise on RCTs
Wednesday, September 12th: Review HW & Observational Studies
- Read & Work through QSS Chapter 2.5- 2.6
- Assignment due: QSS Exercise 1.5.1
- Download data for Assignment here
Friday, September 14th:
- Homework: SWIRL Exercise CAUSALITY 2
- Code from in class exercise
Monday, September 17th: In Class Exercise on Observational Studies
- Read & Work through QSS Chapter 2.5- 2.6
- Slides Week 4
Wednesday, September 19th: Introduction to Measurement – Meeting in 1015
- Read & Work through QSS 3.1 - 3.4
Friday, September 21st: In Class Exercise on Measurement
- Homework finish SWIRL Exercise MEASUREMENT 1
Monday, September 24th: Review HW & Univariate Data Summaries
- Assignment due: QSS Exercise 2.8.3
- Data for Assignment QSS 2.8.3
Wednesday, September 26th: Bivariate Relationships – Meeting in 1015
- Read & Work through QSS 3.5 - 3.7
- Slides Week 5
- Code Bivariate Relationships
- Data Bivariate Relationships
Friday, September 28th: Starting with Writing Assignment 1
- How to write well
- Writing Assignment 1
- Dataset 1 for Writing Assignment 1
- Dataset 2 for Writing Assignment 1
Monday, October 1st: In Class Exercise Measurement & Bivariate Relationships
Wednesday, October 3rd: In Class Exercise Measurement & Bivariate Relationships
Friday, October 5th: Introduction to Prediction
- In-class Quiz no 1
- Read & Work through QSS 4.1
- Submit first draft of Writing Assignment 1 to eCampus
Monday, October 8th: Introduction to Prediction 2
- Submit Peer-Review Comments for Writing Assignment 1 on eCampus
- Read & Work through QSS 4.2
- R code from class
Wednesday, October 10th: In Class Exercise Prediction
- Homework finish SWIRL Exercise PREDICTION 1
- Slides Week 7
Friday, October 12th: Introduction to Regression
- Read & Work through QSS 4.3
- Homework finish SWIRL Exercises PREDICTION 2
Monday, October 15th: In Class Exercise Regression (or Catch up)
- Submit Final Draft of Writing Assignment 1 on eCampus
- Slides Week 8
- Pres 08 Data
- Intrade 08 Data
- 2012 Data
Wednesday, October 17th: Catch-Up Day & Regression Review
- Homework finish SWIRL Exercises PREDICTION 3
Friday, October 19th: Regression and Causality
- Read & Work through QSS 4.3
Monday, October 22nd: Regression and Causality
Wednesday, October 24th: Introduction to Probability
- Read & Work through QSS 6.1
- Slides Probability
Friday, October 26th: Reviewing Probability 1
Monday, October 29th: Conditional Probability
- Read & Work through QSS 6.2
- Assignment due: QSS exercise 4.5.2 Questions 1-5
- Data for Assignment
- Slides Conditional Probability
Wednesday, October 31st: Conditional Probability
Friday, November 2nd: In class Exercise on Probability
- Homework finish SWIRL Exercise PROBABILITY 1
- In-class Quiz no 2
Monday, November 5th: More Probability
- Read & Work through QSS 6.3
- Slides Random Variables
Wednesday, November 7th: Starting Writing Assignment 2
Friday, November 9th: Large Sample Theorems
- Assignment due: QSS exercise 6.6.1 (Questions 1-4)
Monday, November 12th: Law of Large Numbers
- Homework finish SWIRL Exercise PROBABILITY 2
Wednesday, November 14th: Introduction to Uncertainty
- Read & Work through QSS Chapter 7.1.1 - 7.1.4
- Slides Week 13
Friday, November 16th: In Class Exercises about Uncertainty
- Read & Work through QSS Chapter 7.1.5 - 7.1.6
- Homework finish SWIRL Exercise UNCERTAINTY 1
- Submit first draft of Writing Assignment 2 to eCampus
Monday, November 19th: In Class Exercises on Uncertainty
- Read & Work through QSS Chapter 7.1.5 - 7.1.6
- Submit Peer-Review Comments for Writing Assignment 2 on eCampus
- Data Criminal Record
Wednesday, November 21st:
Monday, November 26th: In Class Exercise on Uncertainty
- Homework finish SWIRL Exercise UNCERTAINTY 2
Wednesday, November 28th: Linear Regression with Uncertainty
- Read & Work through QSS Chapter 7.3.1-7.3.4
- Submit Final Draft of Writing Assignment 2 on eCampus
Friday, November 30th: In class Exercise on Regression with Uncertainty
- Read & Work through QSS Chapter 7.3.5-7.4
- Homework finish SWIRL Exercise UNCERTAINTY 3
- Last slides
Monday, December 3rd: Review of Uncertainty
- Assignment due: QSS exercise 7.5.3 (only questions 1, 2, & 4)
- Data Homework
Wednesday, December 5th
- Review for exam
- Prepare questions to review