Introduction to Statistics: Political Science (Class 7)

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
Regression estimates an equation
Coef. SE T P
Party Affiliation (-3strong R 3strong D) 1.286 0.878 1.460 0.143
Voted in 2008 -1.138 1.484 -0.770 0.443
Party Affiliation x Voted in 2008 3.575 0.918 3.900 0.000
Constant 61.100 1.358 44.980 0.000
61.100 1.286Party 1.138Voted
3.575PartyVoted u
61.100 Party1.286 PartyVoted3.575
1.138Voted u
OR
61.100 Party1.286 VotedParty3.575
Voted1.138 u
5
Party Aff. Voted Party Aff. Voted Party x Voted Constant Predicted Value
Coefficients ? Coefficients ? 1.286 -1.138 3.575 61.100
-3 0 -3.858 0 0 61.100 57.242
-2 0 -2.572 0 0 61.100 58.528
-1 0 -1.286 0 0 61.100 59.814
0 0 0.000 0 0 61.100 61.100
1 0 1.286 0 0 61.100 62.386
2 0 2.572 0 0 61.100 63.672
3 0 3.858 0 0 61.100 64.959
Party Aff. Voted Party Aff. Voted Party x Voted Constant Predicted Value
Coefficients ? Coefficients ? 1.286 -1.138 3.575 61.100
-3 1 -3.858 -1.13775 -10.7258 61.100 45.378
-2 1 -2.572 -1.13775 -7.1505 61.100 50.240
-1 1 -1.286 -1.13775 -3.57525 61.100 55.101
0 1 0.000 -1.13775 0 61.100 59.962
1 1 1.286 -1.13775 3.575252 61.100 64.824
2 1 2.572 -1.13775 7.150504 61.100 69.685
3 1 3.858 -1.13775 10.72576 61.100 74.547
6
(No Transcript)
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 X1 and Y does not
  depend on the value of X2, 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. Whats 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 Contacted Contacted
Did not Vote 374 (33.8) 124 (12.5)
Voted 731 (66.2) 870 (87.5)
19
DVTurnout

• 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 dont 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 29th
• Erica 7-10 Luis 2-4