Department of Political Science Professor Loren Collingwood
T 9:10 – 12:
Watkins Hall 2145 Office: 2229 Watkins Hall
Office Hours:
T 2:00 – 4:00 pm
Political Science 202A
Survey of Quantitative Methods
Fall 2016
Description
This course is an introduction to quantitative methods in political science. However, the reasoning underlying the scientific method and the use of statistics can be applied even to situations in which quantitative data are unavailable. This class is designed to provide an introduction to the basic statistics that are necessary for understanding and evaluating political relationships. As such, the course emphasizes hands on data work and seeks to transform students from passive consumers of data to critical analysts. We will focus primarily on introductory statistical methods, including, but not limited to, descriptive statistics, probability, sampling distributions, parameter estimation, hypothesis testing, correlation, and bivariate regression analysis. Students will also spend considerable time learning the statistical computing language, R. This course is a prerequisite for POSC 202B.
This class is hard. You will spend much of the time feeling frustrated. But your ability to solve problems creatively will be enhanced. Perseverance is necessary for success!
Required Texts:
Moore and McCade. Introduction to the Practice of Statistics, 5th Edition
Suggested Texts:
Dalgaard, Peter. Introductory Statistics with R, 2nd Edition
Zuur et al. A Beginner’s Guide to R
Course Requirements
Firsts, students must come to seminar each week. If you will miss seminar, please email me or otherwise let me know in advance. Please consult the course website for updates and assignments:
Students should bring a laptop to each course. R shall be installed on your laptops. This may prove a challenge for some of you. It is good for your character. You can install R from here: Once you install R you may also want to install RStudio ( as an IDE (integrated development environment). Note you have to install R first! RStudio will probably make coding and doing statistics easier.
Some useful R websites:
Introductory statistics and data manipulation:
Graphics using ggplot:
How to be cool with R:
Grading
Grading for the course is based on five homework assignments and one final paper/project. The total number of points is 100. Homeworks are due at the beginning of class and will be a combination of written homework
Final Paper/Project: 50
Homework: 50 (5 assignment, 10 points each)
Grading Scale
A = 94 – 100 %
A- = 90 – 93 %
B+ = 87 - 89 %
B = 84 - 86 %
B- = 80 – 83 %
C+ = 77 – 79 %
C = 74 – 76 %
C- = 70 – 73 %
D+ = 67 – 69 %
D = 64 – 66 %
D- = 60-63 %
F = 59% and below
Plagiarism and Academic Misconduct
This section is taken from the Academic Integrity Brochure for Students (
“At the University of California, Riverside (UCR) honesty and integrity are fundamental values that guide and inform us as individuals and as a community. The academic culture requires that each student take responsibility for learning and for producing work that reflect their intellectual potential, curiosity, and capability. Students must represent themselves truthfully, claim only work that is their own, acknowledge their use of others’ words, research results, and ideas, using the methods accepted by the appropriate academic disciplines and engage honestly in all academic assignments. Misunderstanding of the appropriate academic conduct will not be accepted as an excuse for academic misconduct. If a student is in doubt about appropriate academic conduct in a particular situation, he or she should consult with the instructor in the course to avoid the serious charge of academic misconduct.”
Plagiarism is the “copying of language, structure, or ideas of another and attributing (explicitly or implicitly) the work to one’s own efforts. Plagiarism means using another’s work without giving credit. Examples include but are not limited to:
●Copying information from computer-based sources, i.e., the Internet
●Allowing another person to substantially alter or revise your work and submitting it entirely as your own.”
Other forms of academic dishonesty include cheating by “copying from another student’s examination, quiz, … or homework assignment.” Note that the definition of cheating also includes “submitting for academic advancement an item of academic work that you have previously submitted for academic advancement” without prior authorization from the faculty member supervising the work. “Unauthorized collaboration” is also considered inappropriate.
If I suspect you have committed an act of academic misconduct, I will discuss it with you and file a report with the Student Conduct & Academic Integrity Programs (SCAIP). You may receive a zero on the assignment and an “F” for the course. Further disciplinary action may also be taken by SCAIP. If you ever have a question about plagiarism or other academic conduct, please ask me before you turn in any work that may be problematic.
Students with Disabilities
If you have a physical, psychiatric, emotional, medical, or learning disability that may impact your ability to carry out assigned coursework, I urge you to contact the staff in Student Special Services (), who will review your concerns and determine, with you, what accommodations are necessary and appropriate. All information and documentation are confidential.
IMPORTANT DATES:
December 2: Final Paper due by 5pm.
Week / Dates (week of) / Topics / ReadingsWeek 1 / 09/27/16 / Why Statistics; Describing Data; Coming to grips with R / Moore and McCabe (pp. 4-6; 3.1)
Week 2 / 10/04/16 / Data and Distributions / M&M (1.1, 1.2, 3.3, 3.4)
Week 3 / 10/11/16 / Probability I
Homework #1 Due (10/11) / M&M (4.1-4.4)
Week 4 / 10/18/16 / Probability II
Homework #2 Due (10/18) / M&M (4.5, 5.2, 5.1)
Week 5 / 10/25/16 / Tests of Significance / M&M (6.1, 6.2, 6.3, 6.4)
Week 6 / 11/01/16 / Inference
Homework #3 Due (11/01) / M&M (7.1, 7.2, 8.1, 8.2)
Week 7 / 11/11 / Relationships among Variables / M&M (9.1, 9.2, 2.2)
Week 8 / 11/08/16 / Data Graphics
Homework #4 Due (11/08) / Moore and McCabe (2.1)
Week 9 / 11/15/16 / Regression I / M&M (10.1)
Week 10 / 11/22/16 / Regression 2
Homework #5 Due (11/22) / M&M (10.2)
Week 11 / 11/29/16 / Final Paper Due 12/02, 5pm / Work on Final Paper. Review Topics. Questions