1. Introduction to Statistics: Political Science (Class 7) Part I: Interactions Wrap-up Part II: Why Experiment in Political Science?

2. Why use an interaction term? Theoretical reason to think the relationship between one potential IV and the DV depends on the value of another IV

3. Was CER turned into a partisan issue by political rhetoric? DV: Support for Comparative Effectiveness Research (CER) – ranges from 0 “strongly oppose” to 100 “strongly support” We think the relationship between party affiliation and support depends on whether an individual is politically engaged (we measure this using “voted in 2008”)

4. Coef.SETP Party Affiliation (-3=strong R; 3=strong D)1.2860.8781.4600.143 Voted in 2008-1.1381.484-0.7700.443 Party Affiliation x Voted in 20083.5750.9183.9000.000 Constant61.1001.35844.9800.000 61.100 + 1.286*Party – 1.138*Voted + 3.575*Party*Voted + u 61.100 + Party*1.286 + Party*Voted*3.575 – 1.138*Voted + u 61.100 + Party(1.286 + Voted*3.575) – 1.138*Voted + u 61.100 + Party*1.286 + Voted*Party*3.575 – Voted*1.138 + u 61.100 + Party*1.286 + Voted(Party*3.575 –1.138) + u OR Regression estimates an equation…

5. Party Aff.VotedParty Aff.VotedParty x VotedConstantPredicted Value Coefficients  1.286-1.1383.57561.100 -30-3.8580061.10057.242 -20-2.5720061.10058.528 0-1.2860061.10059.814 000.0000061.100 101.2860061.10062.386 202.5720061.10063.672 303.8580061.10064.959 Party Aff.VotedParty Aff.VotedParty x VotedConstantPredicted Value Coefficients  1.286-1.1383.57561.100 -31-3.858-1.13775-10.725861.10045.378 -21-2.572-1.13775-7.150561.10050.240 1-1.286-1.13775-3.5752561.10055.101 010.000-1.13775061.10059.962 111.286-1.137753.57525261.10064.824 212.572-1.137757.15050461.10069.685 313.858-1.1377510.7257661.10074.547

7. Why/how does this work? Remember: OLS “blindly” identifies the coefficients on the IVs you specify that minimize the sum of the squared residuals If the relationship between X 1 and Y does not depend on the value of X 2, then the coefficient on the interaction will be 0 because that will lead to the best fit!

8. Why Experiment?

9. Two primary threats to identifying causal relationships Reverse causation –If we find an association, what causes what? Confounding / missing variables –Unaccounted for factors that might lead to biased estimates of the relationship between an explanatory variable and outcome

10. Experimental data Emphasis on the data gathering process Randomized intervention –Defining characteristic of experiments. What’s so great about it?

11. The logic of random assignment If each of you were to roll a die and: –Be assigned to group 1 if you roll a 1, 2, or 3 –Be assigned to group 2 if you roll a 4, 5, or 6 On average, how would two groups differ?

12. Benefits of Random Assignment Random assignment ensures that treatment and control groups will be similar except for the fact that one group is “treated”

13. Does media bias affect party attachments? Observational (survey) –What is your main source of TV news? –Fox News: 63% Republicans, 22% Democrats –CNN: 25% Republicans, 63% Democrats If we run a regression predicting party identification with main news source as the independent variable… –Missing variables? –Reverse causation?

14. Does media bias affect attitudes? Experiment – recruit a bunch of New Haven residents –Randomly assign to watch: A conservative news program OR A liberal program OR A placebo or nothing –Measure issue attitudes Compare attitudes across groups

15. Media Experiment What confounds would we account for? Treatment is – by design – not correlated with anything else. So no confounds! Is reverse causation a problem?

16. External validity Limits of examining effect of media bias on party attachments “in the lab”? –Is this how people really watch TV? –Is one “session” enough? –Demand effects? –Is the sample likely to be affected in a unique way?

17. Do GOTV efforts work? During a presidential election year, campaigns spend loads of money on efforts to get people to vote But how do we know if they work? One possibility: survey people –Ask if they were contacted –Ask if they voted

18. Do GOTV efforts work? Not ContactedContacted Did not Vote374 (33.8%) 124 (12.5%) Voted731 (66.2%) 870 (87.5%)

19. DV=Turnout Predictor Coef SE T P Contacted 0.214 0.018 11.87 0.000 Constant 0.662 0.012 53.38 0.000 Being contacted increases the probability that someone will turnout by 21%???? –What else could explain (confound) this relationship?

20. GOTV: lab or survey experiment Lab or survey experiment: embed a randomized treatment (text) in a survey Effects of GOTV messages: –Randomly present some people with a message encouraging them to vote and not others –Ask them how likely they say they are to vote –See if people presented with the message say they are more likely to vote Strengths of this? Weaknesses?

21. GOTV: field experiment Field experiment: intervention done while people are going about their business Effects of GOTV messages: –Randomly send some people on the voter rolls a message encouraging them to vote and not others. –Check the voter rolls after the election and see if people who were sent a message were more likely to vote.

22. Benefits of Field Experiments What are some of the benefits of a field experiment like this? Big one: External validity

23. Toolbox Multivariate regression and experiments are two ways to attempt to make inferences about causality Benefits of observational analysis: –Can “find” data – don’t have to gather it yourself –Sometimes the only reasonable approach (What causes wars? How does GDP affect infant mortality?)

24. Toolbox Costs of observational: –Difficult (impossible?) to definitively determine causation Did we measure every possible confound? Did we specify the controlled relationships properly? What causes what?

25. Baby, bathwater This does not mean that multivariate regression is useless! –If we think carefully about what the right regression model should be… we can get to pretty darn good (i.e., defensible) estimates This means think theoretically: –Do we have strong prior expectation that X causes Y, rather than Y causing X? –What factors might confound our estimates?

26. Next time How much do get out the vote efforts increase turnout? Analyzing data from political experiments Homework 2 due today Homework 3 due Tuesday after break (11/30) –TA office hours: All TAs will have OH on Monday, the 29 th Erica: 7-10 ; Luis: 2-4