Introduction to Statistics: Political Science (Class 1)

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Introduction to Statistics Political Science
(Class 1)

• Answering Political Questions with Quantitative
  Data (political variables, review of bivariate
  regression, thinking about causality)

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Why learn how to answer political questions with
quantitative data?

• Area to apply/practice using statistics
• Tools can be applied elsewhere (on the job,
  health decisions Atkins/gluten free?)
• Understand cause and effect in politics
• Academic reasons develop knowledge that can be
  passed on to others
• As a citizen evaluate evidence about policies
  who deserves credit/blame
• Prepare for your future responsibilities as
  political officials???

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What types of questions can data analysis help us
to answer?

• International relations
• Why do countries go to war?
• Comparative politics
• Why does the rate of infant mortality vary across
  countries?
• Policy
• How can we improve student test scores?
• Public opinion/political behavior
• How do people decide whether to vote?
• What policies does the public support and why?

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Todays agenda

• Measuring political concepts
• Review of bivariate regression
• Thinking about causality

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Measurement Units of analysis

• What are the cases/rows in political data?
• Actors individuals, elected officials
• Geographic/political units states, countries,
  precincts
• Events individual congressional races, elections
  (e.g., seats won), court cases
• Unit/Time country-year, individual at time T

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Measurement Data Sources

• Government / historical records
• Vote by precinct GDP/economic data individual
  turnout
• Expert assessments
• Level of democracy presidents personalities
• Surveys
• Reported attitudes / behaviors

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For example .

• Distribution of a variable in politics
• What is this margin of error /- 3?

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Relationships between variables (regression
analysis)

• Two types of variables
• Dependent variable (or predicted variable or
  regressand) what we want to predict
• Independent variable (or explanatory variable or
  regressor)
• Bivariate regression model
• ? ß0 ß1X u

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How does presidential approval affect midterm
election outcomes?

• Unit of analysis midterm election (1950-2006)
• Dependent variable seats gained by incumbent
  presidents party (House)
• Independent variable presidential approval on
  Labor Day of election year
• 0 (no one approves) 100 (everyone approves)

Coef SE Coef T
P Presidential Approval 1.32
0.50 2.64 0.020 Constant
-93.32 27.28 -3.42 0.005
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? ß0 ß1X
u Seats -93.32 (1.32 Approval) u
Remember in regression analysis (aka Ordinary
Least Squares), the best fit line is the one
that minimizes the sum of the squared residuals
-15
In 1978, Carters approval was 49()
Obamas approval rating was 46()
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Democratic Peace

• Theory Democracies tend not to go to war with
  one another why would this be?
• What does a democracy look like? How could we
  measure democracy?

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Polity III Democracy score (0-10)

• Competitiveness of Executive Recruitment
• Selection (e.g., hereditary, military-based,
  rigged) (0 points)
• Dual/Transactional (one hereditary/one by
  elections) (1 point)
• Election (2 points)
• Constraints on Chief Executive
• Unlimited Authority (0 points)
• Substantial limitations (2 points)
• Parity/Subordination (4 points)
• Openness of Executive Recruitment
• 0 or 1 point
• Competitiveness of participation
• Repressed/no participation (0 points)
• Factional (ethnic/parochial factions battle it
  out 1 point)
• Transitional
• Competitive (stable and enduring secular
  political groups compete for political influence
  at the national level 3 points)

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Democracy ? Peace?

• Units of analysis country-dyad-years
• Restricted to relevant dyads (1945-2008)
• Dependent variable number of years the pair of
  countries have been at peace
• Independent variable sum of countries democracy
  scores (0-20)

Coef SE Coef T
P Democracy Scores 0.259 0.023
11.34 0.000 Constant 23.21
0.253 91.82 0.000
Why are these SEs so small / T values so big???
N35,554
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Causal relationships

• Identifying associations is nice, but usually we
  want to identify causality
• Two primary threats
• Reverse causation
• If we find an association, what causes what?
• Confounding / missing variables
• Additional factors that might lead us to give too
  much credit to an explanatory variable

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Reverse Causation?
Lets say we have some survey data
?
Contact by a Political Campaign
Intent to Vote
NOTE Solid lines proposed causal relationship
dotted lines non-causal correlation
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Missing variable?
?
Forest Fires
Ice Cream Sales
NOTE Solid lines proposed causal relationship
dotted lines non-causal correlation
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Presidential Approval ? Midterm Outcomes
Presidential Approval (Labor Day before election)
Midterm Outcomes
What else might explain midterm outcomes? Were
we giving too much credit to presidential
approval ratings as an explanation in our
bivariate analysis?
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Democracy ? Peace?
Pair of Countries (do not) Go to War
Level of Democracy in Pair of Countries
Explanations for lower likelihood of war that
might confound the relationship between democracy
and peace?
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For the next few weeks

• Thinking about and accounting for more than one
  possible explanation
• Next 4 classes using multivariate regression to
  deal with known, measured confounds
• Later dealing with unknown confounds and reverse
  causation

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Goals

• By the end of the semester you will be...
• ...able to conduct and interpret multivariate
  regression analysis and analyze experimental data
• ...better prepared to understand quantitative
  findings reported in political science (and
  other) research
• ...able to think critically about and recognize
  the strengths and weaknesses of these analyses

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Grading/expectations

• No new books but youre encouraged to have a
  book
• 4 homework assignments
• Conduct and interpret analysis
• Think about how analyses could be improved
• Participation
• If you dont understand, ask!
• The final about 1/3 focused on first segment of
  the class, 2/3 on this segment

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Note on next week

• First homework assignment will be handed out this
  Thursday. Due next Thursday.
• No class next Tuesday
• TAs will hold extra office hours on Monday
  (November 1st see syllabus for times)
• Take a look at the homework before Monday you
  may need help!

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Next time (Thursday)

• What multiple regression analysis (regression
  with more than one explanatory variable) can get
  us