Some things to talk about

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Some things to talk about

• Social and political polarization
• A cool dynamic network simulation (which we
  havent done yet)
• Statistical cutoffs and p-values (work of Wald,
  Berger, )
• Survey weighting and poststratification

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Studying social and political polarization

• Andrew Gelman
• Departments of Statistics and Political Science,
  Columbia University
• 7 Feb 2009
• Also Tian Zheng, Thomas DiPrete, Julien
  Teitler, Jiehua Chen,Tyler McCormick, Rozlyn
  Redd, Juli Simon Thomas, Delia Baldassarri, David
  Park, Yu-Sung Su, Matt Salganik, Duncan Watts,
  Sharad Goel

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Studying social and political polarization

• Questions from sociology
• Questions from political science
• Sources of data
• Statistical challenges

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Questions from sociology

• The degree distribution
• Characteristics of the social network
• Homophily
• Quantifying segregation
• Knowing and trusting

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Questions from political science

• Polarization of Democrats and Republicans
• Polarization of political discourse
• How are people swayed by news media, talk radio,
  each other,
• Geographic polarization
• Polarization and the perception of polarization

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Sources of data

• Complete data on small social networks (schools,
  monks, )
• Very sparse data on large social networks
  (Framingham, )
• Complete data on other networks (scientific
  coauthors, )
• Other network datasets (email, Facebook, )
• From random sample surveys
• Questions about close contacts (GSS 1985/2004,
  NES 2000)
• Questions about acquaintances (How many Xs do
  you know?)

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Statistical challenges Misconceptions of others

• Examples
• Name
• Disease status
• Sexual preference
• Political leanings
• Challenge/opportunity attributed and perceived
  attributes
• Appearance vs. reality
• How large is the footprint of a group?

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Statistical challenges Learning about small and
large groups

• 1500 respondents x 750 acquaintances 1 million
• Potential to learn about small groups
• Potential to learn about people you cant
  interview
• Difficulty with large groups
• For example, How many Democrats do you know
• known is too high to quickly estimate
• Potential solution look at subnetworks
• Cube model (individuals x groups x subnetworks)
• Need main effects and two-way interactions

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Statistical challenges Network structure

• Social network is patterned
• Sex, age, ethnicity, SES, location
• Names, occupations, attitudes
• Correct for non-uniform patterns by using a mix
  of names
• Estimate non-uniform patterns using a conditional
  probability matrix for ages
• Overdispersion to model unexplained variation
• Cant do much with triangles, 4-cycles, etc.

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Statistical challenges Recall bias

• Some people are easier to recall than others
• David, Olga, Sharad
• For some sets of names, can be quantified
  Nicole/Christine/Michael
• Sliding definitions
• Who are your friends?
• Estimates of average known range from 300 to 750
  to
• Estimates of average trusted range from 1.5 to
  15 to 150

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Statistical challenges Returning to the social
science questions

• Polarization as political segregation in the
  social network
• Comparing polarization to perceived polarization
• Answering conjectures such as People in big
  cities know more people but trust fewer people
• Getting geography back in the picture

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Forming Voting Blocs and Coalitions as
aPrisoner's Dilemma A Possible
TheoreticalExplanation for Political Instability

• Andrew Gelman
• Departments of Statistics and Political Science,
  Columbia University
• 7 Feb 2009

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Dynamic network model for political coalitions

• Mathematics of coalitions
• Forming a coalition helps the subgroup (or they
  wouldnt do it)
• But it hurts the general population (negative
  externality)
• Coalitions are inherently unstable
• Coalitions of coalitions
• Opportunistic acts of secession, poaching, and
  dissolution
• The simulation I want to do
• Set up a political settings agents with
  attributes and locations
• Payoff function for agents
• Locally optimal moves
• Scheduling
• Implementation

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Statistical cutoffs and p-values

• Andrew Gelman
• Departments of Statistics and Political Science,
  Columbia University
• 7 Feb 2009

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Setting a cutoff for selecting patterns for
further study

• Old problem in statistics Neyman, Wald, Berger,
  
• Also of interest to biologists!
• Some different goals
• Finding patterns that are statistically
  significant
• Classifying into those to study further, and
  those to set aside
• Mathematical framework distribution of a
  score
• Solution depends upon
• Distribution of the score among uninteresting
  cases
• Distribution of the score among interesting
  cases
• Number of uninteresting and interesting cases
• Cost of follow-up of uninteresting cases
• Cost of follow-up of interesting cases

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Survey weighting and poststratification

• Andrew Gelman
• Departments of Statistics and Political Science,
  Columbia University
• 7 Feb 2009

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Survey weighting and poststrafication

• General framework for adjusting for differences
  between sample and population
• Population estimate avg over poststratification
  cells
• You might have to model
• The survey response
• Size of poststratification cells
• Probabilities of selection
• Respondent-driven sampling example
• Cells determined by gregariousness and
  distance
• Could approx correlations using clustering