1. Political Analysis II

2. Course Overview Weekly lab sessions: Assessment:
Week 2: Model Specification
Week 3: Interaction Effects
Week 4: Analysis of Experimental Data
Week 5: Inference
Week 6: Model Diagnostics and Assumptions of OLS
Week 7: Quantities of Interest
Week 8: Logistic Regression
Assessment:
Option 1: Take-home exam
Option 2: Essay based on data analysis (up to 2000 words)
Consultation by the end of week 9 (8 December 2017)
Deadline: 12PM Friday 26 January 2017 (HT week 2)

3. First: getting started
Clear your workspace

4. Quick revision I

5. Quick revision II Equation for a straight line y=mx+b (or y=b+mx)
Thus, the equation we estimate to describe the relationship between x and y takes the form:
data = model + residuals
data = intercept + coefficient*IV + error

6. Quick revision III

7. Model Specification

8. What to control for?
Multivariate regression allows us to control for confounders or other possible causes.
Theory tells us what these things are.
Two strategies for thinking of confounding variables:
Think of things that affect y but are not in the regression model, and then ask yourself whether they might be related to x.
Think of things that affect x (or things that are related to and precede x) but are not in the regression model, and then ask yourself whether they might be related to y.

9. What to control for?
Absence of control variables can lead to omitted variables bias

10. Omitted-Variables Bias
Bias: expected value of parameter estimate from sample =/= true population parameter
Omitted-variables bias: bias resulting from failure to include a variable that belongs in the model
So which variables can we omit?
Can omit Z if completely unrelated to Y
Can omit Z if completely unrelated to X
Both of which are unlikely

11. Omitted-Variables Bias
Failing to control for relevant variables can lead to mistaken causal inferences for variables we do include
Consider this when doing own research, and when reading research articles/books
Can you think of any other independent variables that are likely to be related to both x and y?

12. What NOT to control for? Consequences of the treatment variableX Z Y
Can lead to post-treatment bias
Effect of party ID on vote choice
Do control for race
Do not control for last-minute voting intentions
Effect of medicine on health
Do control for health prior to treatment decision
Do not control for side effects of treatment

13. Think Carefully…
Careful theory: tells us which variables to include

14. Exercise
Let‘s look at the association between a student‘s height and his/her spelling skills.
On the next page you will find a plot and a regression output. The dependent variable is the obtained score in a spelling test (0-100, where 100 is max), while the independent variable is the height of the student.
Ask yourself:
Why do we see this association?
Are there any other variables that we need to include that influence both a student’s height and his/her spelling skills?

15. Exercise (2)

16. Exercise (3)
We now control for the grade in which students are in (Grade 1, Grade 3 or Grade 5), which changes the result. The new output is seen on the next page.
Q: Why do we see this change?
A: The grade in which students are in reflects their

17. Exercise (4)

18. Examples Comparative Government International Relations
Tsebelis, G. and Nardi, D.J. (2014) ‘A long constitution is a (positively) bad constitution: Evidence from OECD countries’, (Data: )
Ross, M. (2006) ‘Is democracy good for the poor?’, American Journal of Political Science, 50(4), (Data:
International Relations
Maoz, Z., and Russett, B. (1993) ‘Normative and structural causes of democratic peace, ’, American Political Science Review, 87, 624–638.
Gartzke, E. (2007) ‘The capitalist peace’, American Journal of Political Science, 51, 166–191.
Political Sociology
Jacobsmeier and Lewis (2013) ‘Barking up the wrong tree: Why Bo didn’t fetch many votes for Barack Obama in 2012’, PS: Political Science & Politics, 46(1),
Inglehart and Norris (2003: Chapter 5)
Clarke (2009) ‘The phantom menace: Omitted variable bias in econometric research’.