ASTK15409U  COURSE: Advanced Quantitative Methods in Political Science

Volume 2015/2016
Education

Bachelor level: 10 ECTS

Master level: 7,5 ECTS
 

Elective course - SRM

Bachelor students can only sign up for this course if they are enrolled at political science

Content

This course introduces graduate students to quantitative methods in political science. During the first half of the course, the course focuses on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The course will be structured according to the following headlines:

  1. Visualizing Data.

  2. Getting data from the Web

  3. Fundamentals of Probability.

  4. Sampling and Statistical Inference

  5. Linear Regression: Dummies and Interactions, Inference and Hypothesis Tests.

  6. Linear Regression: : Interpreting Substantive Effects via the Simulation Method, Diagnostics

  7. Non-linear probability models - The likelihood theory of statistical inference

  8. Binary data

  9. Instrumental variable estimation (IV-estimation)

  10. Regression Discontinuity Design (RDD)

  11. Panel models: Pooled regression, Random effects, Fixed effects

  12. Missing Data

  13.  

The course will primarily support and use two software packages in the course: R and Stata. For the majority of problems, R will be the software package of choice.

Learning Outcome

The main goals of this course are to enable students to develop sound critical judgment about quantitative studies of political problems, to interpret quantitative analyses in published work, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model.

 

Since the aim of the course is to enable students to conduct their own statistical analysis, the course is a good basis and starting point for any other project in the program involving statistical methods. In addition, the course is highly relevant for any student who aims for a career, which involves data analysis

The course will not use a single textbook. Selected readings will be made available at the start of the course. In general the following books are useful for this course:

 

Wooldridge, Jeffrey. 2009. Introductory Econometrics: A Modern Approach. 4th edition. South-Western College Pub.

 

Kennedy, Peter. 2008. A Guide to Econometrics. 6th edition. Blackwell Publishing.

 

Fox, John. 2008. Applied Regression Analysis and Generalized Linear Models. 2nd edition. Sage.

 

King, Gary. 1989. Unifying Political Methodology. Ann Arbor: University of Michigan Press.

There is formally no prerequisite for this course except an open mind and a good command of high school algebra.
The course is divided into a lecture-style seminar (Multivariate Analyses) and a computer lab session (Tutorial Multivariate Analyses). During the computer lab session, students will apply the statistical models introduced in the lecture. The lab sessions will be devoted to learning the various commands in R and Stata and apply the statistical models from the lecture to selected political science data sets. The data sets that we will use cover the major fields in political science.
Credit
7,5 ECTS
Type of assessment
Written assignment
Written assignment
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assesment
  • Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
  • Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
  • Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner
  • Category
  • Hours
  • Class Instruction
  • 28
  • Course Preparation
  • 50
  • Exercises
  • 20
  • Exam
  • 108
  • Total
  • 206