Political
Science 446/546
Methods for Political Analysis II
Professor Genie Baker
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Class |
Office |
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Spring 2008 TR 12-1:20 |
Hours: TR 1:45-3:15 & by appt. |
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905 PLC |
920 PLC, 346-4623 |
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genie@uoregon.edu |
Course Objectives: This course is more aptly titled “Statistical Methods for Political Analysis II.” It is a continuation of PS 445/545, and will introduce the theory, practice, application, and some extensions of linear regression analysis. The goal is to provide students with the background necessary to design and implement studies involving regression, to read and evaluate literature that uses regression methods, and to pursue more advanced training.
The course will balance statistical theory with applied methodology. A central theme will be the integration of statistical analysis and substantive context. It is hoped that students move beyond cookbook thinking and toward careful thought about the connections between formal statistical concepts and their implications in applied contexts.
Course prerequisites: Students are assumed to have a working knowledge of the basic statistical concepts and techniques covered in PS 445/545: descriptive statistics, elementary probability theory, sampling distributions, statistical inference and classical hypothesis testing, and bivariate regression analysis.
Course requirements and grading
· Homework assignments, 30%: Homework assignments will include both problems to be discussed in class and homework sets to be turned in for grades. The latter will include pencil-and-paper exercises and computer-based data exercises. Students are welcome to think through their homework together, but should write up their own assignments, showing all work. In my experience, students who do not keep up with the homework, learn the statistical software, etc., meet with unpleasant fates on the exams.
· In-class midterm, 30%: Tuesday, May 6th.
· Take-home final, 40%: Due date TBA (distributed last day of class, due during exam week).
Recommended Texts:
David Freedman, Robert
Pisani & Roger Purves (1998) Statistics,
3rd Ed.
Damodar N. Gujarati
(2003) Basic Econometrics (4th Edition).
Jack Johnston and John
DiNardo (1997) Econometric Methods
(Fourth Edition).
Peter Kennedy (1998) A Guide to Econometrics (Fourth
Edition).
G.S. Maddala (2001) Introduction to
Econometrics, 3rd ed. NY: Wiley.
Larry D. Schroeder, David
L. Sjoquist and Paula E. Stephan (1986) Understanding
Regression Analysis: An Introductory Guide.
Gujarati provides the most accessible
introduction to most of the material we will be covering. All of these books cover much of the same
material, though, and different readers tend to have different
preferences. As the books are very
expensive, I would be prepared to look through them at the library and decide
which you find most helpful.
Additional readings will be made available on Blackboard.
Software:
Homework assignments will
require the use of statistical software.
I will provide a demonstration and examples using Stata, but students
are free to use another package if they can do so independently and are willing
to accept the risks involved. There is a
$20 lab fee for access to the Social Sciences Instructional Lab (SSIL).
TOPICS AND
Week 1. Introduction
A. Topics:
Why
we’re here
Descriptive
vs. Inferential statistics
Types
of Variables
Describing
single variables
Summation
Operators
Freedman,
Pisani & Purves: Chapter 4
Gujarati:
Introduction (pp. 1-14), Appendix A1
Maddala,
pp. 15-17
B. Introduction to Stata, No readings.
Week 2. Review: Describing Relationships & Basic
Probability Theory
A. Topics:
Describing
relationships between variables:
Difference
of means
Covariance,
correlation & regression
Analyzing
tables
Confounding
Freedman,
Pisani & Purves: Chapters 8-12 (Correlation and Regression)
Schroeder et. al., pp. 11-29 (Regression Analysis)
Christopher
H. Achen (2000) “Warren Miller and the Future of Political Data Analysis.” Political
Analysis 8,2: 142-146 (SKIM --
The lost art of analyzing tables)
Angus Campbell, Philip E.
Converse, Warren E. Miller and Donald E. Stokes (1964) The American Voter,
abridged edition.
B. Topics:
Basic
Probability Theory
Mathematical
Expectation
Histograms
& Probability Distributions
Start
inferential statistics: inferences about a single mean
Maddala,
2.1-2.11
Freedman,
Pisani & Purves: Chapters 3, 13, 14-18
Gujarati:
Introduction (pp. 1-14), Appendix A1-A5
Week 3. Inferential
Statistics: Relationships Between Variables
A. More Inferential Statistics
Topics:
The
Central Limit Theorem
Difference
of means tests
Chi-squared
test
Tests
related to regression analysis
Freedman,
Pisani & Purves, ch. 16-18
(Brilliant,
deceptively simple presentation of the Central Limit Theorem)
Neil Weiss (2002) Introductory Statistics, 6th ed.
Freedman, Pisani &
Purves, ch. 28 (Chi-Squared Tests)
Gujarati, Appendix A6-A8 (Inference)
Gujarati, Chapter 3 (Regression Inference)
Johnston
and DiNardo: Appendix B.2-B.4
B. Regression & Inference
Topics:
More
Regression: Inference, Prediction & Presentation
Maddala,
5.5 & 5.6
Schroeder
et. al.: pp. 29-31
Gujarati:
7.8
Johnston
and DiNardo: 3.2
Kennedy:
5.5
Christopher H. Achen (1977) “Measuring
Representation: Perils of the Correlation Coefficient.” American Journal of Political Science 21:
805-815.
Gary King, Michael Tomz,
and Jason Wittenberg (2000) “Making the Most of Statistical Analyses: Improving
Interpretation and Presentation.” American
Journal of Political Science 44, 2 (April): 347-61.
Week 4. Regression
Diagnostics, Multiple Regression, Begin Matrix Algebra
A. Regression Diagnostics
Topics:
Outliers
Nonlinearities
Maddala,
sections 3.5-3.9
Gujarati:
Chapters 4-6 (skip or skim 4.4, skip 5.9 for now)
Johnston
and DiNardo: 1.5-1.8
John Fox (1991) Regression
Diagnostics. NY: Sage, pp. 21-40 (Analysis of outliers, influence
diagnostics.)
John Fox (1991) Regression
Diagnostics. NY: Sage, pp. 53-60 (Nonlinearities)
Some Applications
Robert W. Jackman ( 1974) “Political Democracy and
Social Equality: A Comparative Analysis.”
American Sociological Review 39,1:29-45.
B. Multiple Regression, Matrix Algebra
Topics:
Introduction to Multiple Regression
Introduction to Matrix Algebra
Maddala,
4.1-4.4
Schroeder
et. al.: pp. 29-31, 32-35
Gujarati,
7.1-7.2
Gary King (1986) “How Not
to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political
Science.” American Journal of Political
Science 30: 666-687.
Maddala,
Appendix to
Gujarati,
Appendix B
Week 5. Regression in Matrix Form; Multicollinearity
and Omitted Variables
A. Multiple Regression in Matrix Form
Maddala,
Appendix to Chapter 4
Gujarati,
Appendix C.1-C.10
Johnston
and DiNardo: 3.1, 3.3-3.5
Kennedy:
50-53, 4.1-4.4
B. Specification Error: Multicollinearity and Variable Selection
Maddala
4.9, ch. 7 (skim 7.5, 7.6, 7.8)
Schroeder
et. al: pp. 65-72
Gujarati:
Chapter 7.7, 10, 13.1-13.4
Johnston
and DiNardo:4.1
Edward E.
Leamer (1983) “Let’s Take the Con Out of Econometrics.” American Economic Review 73,1: 31-43.
Week 6. Midterm Exam, Dummy Variables and
Interaction Terms
A. Midterm Exam
B. Specification Error: Dummy variables, interaction terms and multiplicative relationships
Topics:
As
above
Aside
on t-tests and ANOVA
Maddala,
8.1-8.5
Schroeder:
pp. 53-59
Gujarati:
Chapter 9
Johnston
and DiNardo: 4.6
Thomas Brambor, William Roberts Clark and Matt
Golder (2005) “Understanding Interaction Models: Improving Empirical
Analyses.” Political Analysis, forthcoming.
Tse-min
Lin and Timothy Fackler (1995) “Political Corruption and Presidential
Elections.” Journal of Politics 57,4:
971-993.
Week 7. Catch-up, Heteroscedasticity
Maddala,
5.1-5.4
Schroeder
et. al.: pp. 75-77
Gujarati:
Chapter 11
Johnston
and DiNardo: 6.1-6.3
Kennedy:
8.1-8.3
George W.
Downs and David M Rocke (1979) “Interpreting Heteroscedasticity.” American Journal of Political Science
23,4: 816-828.
Peter
Lemieux (1976) “Heteroscedasticity and Causal Inference in Political
Research.” Political Methodology
3: 287-316.
Week 8. Autocorrelation and Autoregressive Distributed Lag Models
A. Autocorrelation
Maddala,
6.1-6.6
Schroeder
et. al.: pp. 72-75
Gujarati:
Chapter 12
Johnston
and DiNardo: 6.4-6.8, skim 6.9
B. Autoregressive and Distributed Lag Models
Maddala,
6.7-6.10
Kennedy:
pp. 263-270
Gujarati:
Chapter 17 (MORE BELOW)
Johnston
and DiNardo: Chapter 8
Christopher H. Achen (2000) “Why Lagged Dependent
Variables Can Suppress the Explanatory Power of Other Independent
Variables.” Paper presented at the
Annual Meeting of the Political Methodology Section of the American Political
Science Association, UCLA, July 20-22.
Week 9. Simultaneous Equations Models,
Intro to Dichotomous Dependent Variables
A. Simultaneous Equations Models
Maddala,
ch. 18. Skim ch. 19, 20.
Schroeder
et. al.: pp. 77-79
Gujarati:
Chapters 18-20
Johnston
and DiNardo: 9.4-9.6
John
Bound, David A Jaeger and Regina M. Baker (1995) “Problems with Instrumental
Variables Estimation when the Correlation Between the Instruments and the
Endogenous Explanatory Variable is Weak.”
Journal of the American Statistical Association 90,430: 443-450.
Applications:
William
K. Domke, Richard C. Eichenberg and Catherine M. Kelleher (1983) “The Illusion
of Choice: Defense in Advanced Industrial Democracies.” American Political Science Review 77,1:
19-35.
Gary C.
Jacobson (1978) “The Effects of Campaign Spending in Congressional
Elections.” American Political
Science Review 72, 2: 469-491.
B. Dichotomous Dependent Variables: Logit and Probit
Schroeder
et. al.: pp. 79-80
Maddala
8.7-8.10
David W. Hosmer and
Kennedy:
pp. 233-237
Gujarati:
15.1-15.10
Johnston
and DiNardo: 13.1-13.7
Michael
W. Doyle and Nicholas Sambanis (2000) “International Peacebuilding: A
Theoretical and Quantitative Analysis.” American
Political Science Review 94,4:779-801.
Week 10. Finish Dichotomous
Dependent Variables,
Introduction to Maximum Likelihood
Estimation
Jan Kmenta (1971) Elements of Econometrics. NY: Macmillan, pp. 174-182.