ANES Political Data Analysis

Analysis of American National Election Studies (ANES) survey data demonstrating complex political science workflows.

Dataset Overview

ANES 2020 Time Series Study subset focusing on:

  • Political Attitudes: Party identification, ideology, candidate evaluations

  • Demographics: Age, education, income, race/ethnicity

  • Voting Behavior: Turnout, vote choice, political engagement

  • Policy Preferences: Issue positions and priorities

Advanced Features

  • Complex Survey Weights: Proper handling of sampling design

  • Temporal Analysis: Change over election cycle

  • Multiple Imputation: Missing data in sensitive topics

  • Polarization Metrics: Political attitude distributions

Code Example

# Temporal analysis: Political polarization over time
from statqa.analysis.temporal import TemporalAnalyzer

temporal_analyzer = TemporalAnalyzer()
result = temporal_analyzer.analyze(
    data,
    time_var=codebook.variables['interview_date'],
    outcome_var=codebook.variables['party_id_strength'],
    trend_method='mann_kendall'
)

# Test for increasing polarization
print(f"Trend p-value: {result['trend_pvalue']:.4f}")
print(f"Trend direction: {result['trend_direction']}")

Political Science Insights

Q: How has partisan strength changed during the 2020 election cycle?
A: **Partisan Identification Strength**: Significant strengthening trend detected
   (Mann-Kendall τ=0.23, p<0.001). Strong partisans increased from 45% to 62%
   over the pre-election period, indicating heightened polarization.

Files in Example

  • anes_2020_subset.csv: 3,000 respondent subset

  • anes_codebook.csv: Complex variable definitions with survey weights

  • political_analysis.py: Full political science workflow

  • temporal_plots/: Trend visualizations and change point detection