Syllabus
Click here to download a PDF copy of the syllabus.
Course Registration
The course is divided into a lecture-style seminar (Multivariate Analyses) and a computer lab session (Tutorial Multivariate Analyses). During the lab sessions, students will apply the statistical models introduced in the lecture. Students who wish to take the course should register for Multivariate Analyses at the student portal.
Note that this course is highly demanding and entails a substantial work load for students in the form of weekly homework assignments, a mid-term exam and a data essay. Students who wish to audit this class should notify the instructors in advance (participation is subject to free room capacity). Please note that only registered students will receive feedback on their written work.
Teaching Organization: Online and Offline Teaching
Due to the ongoing COVID-19 pandemic, at least some of the course components will take place online. While we plan to return to the campus, not everyone is able to come to class in person. We implement a mix of online and offline teaching to ensure course participation for students who cannot come to campus.
We will start with a Welcome Session on Monday (September 6). This will be online on Zoom.
All office hours will be online on Zoom.
The first week, including the Lecture on Wednesday (September 8) and the first sequence of lab sessions (September 9, 13, and 14) will be online on Zoom.
Starting from September 15:
- The lecture is planned to be in person. The lectures will be either streamed live or recordings will be made available online.
- Two of the three lab sessions will be in person, one lab session will be online on Zoom. We will determine which of the three lab sessions will be online after surveying students in week 1.
Readings
We will not use a single textbook for this course. Selected readings are available on the course ILIAS site (links will be available here as well). The following books will be used in the course:
Fox, John. 2008. Applied Regression Analysis and Generalized Linear Models. 2nd edition. Sage.
King, Gary. 1998. Unifying Political Methodology. Ann Arbor: University of Michigan Press.
Wooldridge, Jeffrey. 2009. Introductory Econometrics: A Modern Approach. 4th edition. South-Western College Pub.
Software
For all calculations, we will support and use the open-source statistical programming language R. It is particularly suited for carrying out state-of-the-art computer-based simulations and data exercises. It can also be used to generate really nice publication-quality visualizations and runs under a wide array of operating systems. R can be downloaded for free at http://www.r-project.org/. Learning R might seem a bit challenging at first, but you will realize that it is incredibly powerful. A readable introduction is given by John Fox’s (2002) An R and S-Plus companion to Applied Regression from Sage. Students with a Stata background can also look at R for Stata Users by Robert A. Muenchen and Joseph M. Hilbe (2010).
A very good graphical user interface for R (which we will also use during the lab sessions) is RStudio. In recent years a growing number of features have been added to this graphical user interface, which makes it the preferred choice for learning R – especially among beginners. It is cross-platform and open-source. RStudio can be downloaded for free at http://www.rstudio.com/. A style guide to make your code easier to read, share, and verify can be found at http://adv-r.had.co.nz/Style.html.
To facilitate an efficient workflow, we will integrate GitHub into the course. Git is a version control system that makes it easy to track changes and work on code collaboratively. GitHub is a hosting service for git. You can think of it as a public Dropbox for code on steroids. We will use it to distribute code and assignments to you. And you will use it to keep track of your code and collaborate in teams. You can find the course on GitHub here.
To get started with R, RStudio and git, please follow the instructions here.
The lab sessions will be devoted to learning the various commands in R and applying 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.
Prerequisites
There are formally no prerequisites for this course except an open mind and a good command of high school algebra.
Course Requirements
You will receive a grade for the seminar Multivariate Analyses and a pass/fail for the lab session Tutorial Multivariate Analyses. Grading will be based on the following components:
The homework assignments will take the form of problem sets, replications, simulations, or extensions of the analyses in class and the lab. The assignments will be handed out after Tuesday Lab, at 18:45, and you are expected to hand in the solution online on GitHub by the next Tuesday 23:59. Late submissions will not be accepted. We provide you with instructions on how to upload your assignments on GitHub in the first week. Homework assignments for Weeks 1 & 2 must be handed in individually. Homework assignments from Week 3 onward must be handed in in groups of 2-3 students. All group members will receive the same grade. Throughout the years, we learned that the only reasonable way to manage the workload in the first semester is to work together. Group work saves you time, as not everyone has to type up his or her own answers. Although it is completely up to you how you share the work, you learn best if everyone tries to answer the problem sets individually first. From our experience, not getting strongly involved in each of the weekly homework assignments lowers group work quality and increases the risk of failing both the mid-term exam and the data essay project. For this reason, indicate about how much percentage points each group member contributed towards the final product. Homework Assignments (pass/fail)
The midterm will be a 90-min closed-book exam that covers the first half of the course materials. Midterm Exam (50 %)
Towards the end of the course, you will work on a data analysis project (we will hand out a data set and problem description). The project will involve the creative application of the statistical techniques to a substantive problem in political science. Your paper should have 2000 words (± 10%, without bibliography). The essay will be marked down if you go below or above the word count. The data essay should emphasize the substantive, statistical, and causal significance of your analysis and the write-up should read very much like the results section of a published article. No collaboration is permitted on the data analysis project. You are welcome to seek advice from the instructors during office hours. Details on the data analysis project will be provided at a later stage. Your data essay is due on DATE by 10:00. Late submissions will not be accepted. Data Essay (50 %)
Communication
We are aware that the current situation complicates communication and group work. To make communication for the course members and among group members as easy as possible, we set up a Slack workspace. Check our ILIAS group for an invitation link. Please only sign-in with your @mail.uni-mannheim.de address.