Applied Statistics for Political Scientists
Department of Politics and International Relations, University of Oxford
Developed by Steve Fisher, Stuart Soroka
(McGill), and Sean Carey.
Instructors: Sean Carey sean.carey@politics.ox.ac.uk
Steve Fisher steve.fisher@sociology.ox.ac.uk
Ken Macdonald kenneth.macdonald@nuf.ox.ac.uk
Lauren McLaren lauren.mclaren@politics.ox.ac.uk
Class Assignments are here.
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The Nuffield College Library’s Taught Course Information website
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Course
Outline
This workshop is the practical, hands-on complement to
the Statistical Methods for the Social Sciences lectures. The aims are to (1) introduce topics and
datasets from the empirical political science literature, and (2) familiarize
students with STATA statistical software, data management, and methods of
statistical analysis.
The course will survey a range of datasets and methods
from across the discipline. Examples
are likely to include: regime type and the chances of war, government duration,
political attitudes, and electoral behaviour.
Exercises and datasets will be studied in conjunction with related
published literature. This course has
been adapted from one originally designed by Steve Fisher and Stuart Soroka.
Students taking the course for credit should remember
that ALL this term's coursework is to be completed and bound together into a
'workbook' or 'portfolio' and handed in to the Politics Graduate Secretary, by the
end of the working day Friday of 0th week Hilary Term 2003.
Week 1 Running and using STATA, including
file management, data input, recoding and transforming data. Graphical exploration and presentation of
data including box-plots, histograms, scatter plots, pie charts and other
methods. [Lecture Notes in pdf]
Week 2 Univariate statistics, including
mean, mode, median, standard deviations, percentiles etc. Tabulating and summarizing data. Hypothesis testing for means (with known and
unknown variance), difference in means and standard deviations of interval
data. Concepts of sampling and random
data generation. [Lecture Notes in pdf]
Week
3 Categorical variables,
contingency tables, notions and measures of association and independence, and
tests for independence. [Lecture Notes in pdf]
Week 4 Linear regression. [Lecture Notes in pdf]
Week 5 Multiple linear regression, including tests for several partial regression coefficients, log transformations, interactions, standardized regression coefficients (betas). [Lecture Notes in pdf]
Week 6 Regression models for categorical variables, and weighting for complex
survey data. [Lecture Notes in pdf]
Week 7 Interpretation of logit and probit models, linear regression
diagnostics, and multicolinearity. [Lecture Notes in pdf]
Week 8 Problems with and advantages of time
series data. Basic time series analysis
common in political science.
Autoregressive distributed lag (ADL) models. [Lecture
Notes in pdf]