research methodology
POL 40170, Fall 2007
Location: Room G5, Daedalus Building, UCD
Time: Wednesday 9 am
SimpleR is an older, much abbreviated version of the course textbook.
To install R on your own computer, go to the R project site, click on “CRAN”, then on any of the mirror sites, then on the name of your computer system (Linux, MacOS, or Windows), and then follow the instructions. You will just need the “base” package. If you have any problems, drop me an email.
If you prefer to have a graphical user interface to R, you might want to try to install Rcmdr. You can do this by running R and typing:
install.packages(”Rcmdr”)
library(Rcmdr)
The class will not make use of Rcmdr, however, and it is not installed on the networked application in the lab.
Here you can find a basic R tutorial on video.
Lecture 1: Univariate descriptive statistics
Assignment 1, due Friday, September 28, 5 pm, by email.
GDP and democracy data: data preparation / analysis / data
Afrobarometer data: data preparation / analysis / data (source: AfroBarometer)
Asylum seekers data: analysis / data (source: OECD International Migration Data)
National Election Study data: data preparation / data / questionnaire (source: Irish National Election Study)
Lecture 2: Multivariate descriptive statistics
Development and democracy data: analysis / data / codebook (source: J.A. Cheibub)
Fake data (A,B,C): analysis
Fake data (firemen): analysis
Example analysis: analysis
Regression explanation plot: analysis (advanced)
An interesting article related to spurious relationships: New evidence for the Theory of the Stork.
Lecture 3: Probability distributions
Assignment 2, due Friday, October 12, 5 pm, by email.
War votes data: data
Slides examples: analysis
Lecture 4: Central Limit Theorem
CLT demonstration: movie
Simulations and bootstrapping: analysis
Lecture 5: Confidence intervals
Assignment 3, due Friday, October 26, 5 pm, by email.
National Election Study data: data / questionnaire (source: Irish National Election Study)
Lecture 6: Hypothesis testing
Slides examples: analysis
Lecture 7: Goodness of fit
Slides examples: analysis (source of election results: Dail Elections since 1918)
Lecture 8: Simple linear regression
Assignment 4, due Friday, November 16, 5 pm, by email.
Slides examples: analysis
Brinks & Coppedge data: data / codebook (source: Michael Coppedge’s Data)
Lecture 9: Multiple linear regression
Patients data: data preparation / data (source: M.H. Kutner, C.J. Nachtsheim, J. Neter, and W. Li, Applied Linear Statistical Models)
Asylum seekers data: analysis / original data / 1999 data (source: ISQ Data Archive; E. Neumayer, Bogus Refugees? The Determinants of Asylum Migration to Western Europe, International Studies Quarterly, 49:3 (2005), pp. 389–410)
Lecture 10: Categorical independent variables
Wages data: demonstration
Freedom House data: demonstration (source: Michael Coppedge’s Data)
NES data: demonstration / data
Lecture 11: Interaction effects
Assignment 5, due Friday, November 30, 5 pm, by email.
Assignment data: data / sample plot (source: Brambor, Clark & Golder replication data)
Lecture 12: Regression diagnostics
Final assignment
Check Gary King, Publication, publication, with advice on how to write a publishable course paper.
Some places where you can find data (among many other):
Political, Social, and Economic Data Sets
Paul Hensel’s International Relations Data Site
Guide to Political Science Data
Political Science Data
IQSS Dataverse Network
World Bank Data
OECD Statistics Portal
Eurostat