Research Methodology

Transparent, rigorous analysis of congressional data from authoritative sources with full methodological disclosure.

Research Approach

Our analysis employs a comprehensive, data-driven approach to examine congressional accountability and electoral competition. We prioritize official government sources, established research institutions, and transparent methodologies to ensure accuracy and reproducibility.

Core Principles

Data Sources

Primary Government Sources

Research Organizations

Key Metrics Definitions

Data Collection Methods

Automated Collection

Where possible, we use automated systems to collect data from official APIs and structured databases. This includes:

• FEC campaign finance data via their REST API
• Congressional voting records from Congress.gov
• Election results from state election offices

Manual Verification

All automated collection is supplemented by manual verification:

• Cross-referencing against multiple sources
• Spot-checking random samples for accuracy
• Reviewing edge cases and anomalies
• Validating calculations against published totals

Historical Data Reconstruction

For historical periods predating digital records, we rely on:

• Official government publications and archives
• Academic datasets with established provenance
• Newspaper archives for election results verification
• Multiple source triangulation for contested figures

Quality Assurance Process

Multi-Stage Validation

1. Source Authentication: Verify all data comes from authoritative, official sources
2. Cross-Reference Check: Compare data across multiple independent sources when available
3. Calculation Review: Independent verification of all mathematical operations and formulas
4. Temporal Consistency: Ensure data series are consistent across time periods and methodology changes
5. Expert Review: Academic and policy expert review of methodology and findings
6. Public Disclosure: Full methodology publication for external scrutiny and replication

Error Detection and Correction

We maintain rigorous error detection protocols:

• Automated range checks for statistical outliers
• Trend analysis to identify potential data breaks
• Regular audits of calculation accuracy
• Public correction process for identified errors

Limitations and Caveats

Data Limitations

• Historical Completeness: Some early data may be incomplete or estimated based on available records
• Definitional Evolution: Some metrics have evolved over time as reporting standards changed
• Sampling Variation: Opinion polling data subject to sampling error and methodological differences
• Reporting Delays: Most recent data may be preliminary pending final official reports

Analytical Limitations

• Correlation vs. Causation: Statistical relationships do not necessarily imply causal relationships
• Predictive Limitations: Historical trends may not predict future outcomes
• Context Dependency: Metrics may be influenced by external factors not captured in the analysis
• Aggregation Effects: National averages may obscure important regional or demographic variations

Scope Limitations

• Federal Focus: Analysis limited to federal elections and institutions
• Quantitative Emphasis: Prioritizes measurable indicators over qualitative assessments
• Institutional Perspective: Focuses on systemic rather than individual-level factors

Updates and Revisions

Update Schedule

Data and analysis are updated on a regular schedule:

• Quarterly: Routine data updates and minor corrections
• Post-Election: Major updates following federal elections
• Annual: Comprehensive methodology review and validation
• Ad-hoc: Corrections for identified errors or significant new data availability

Version Control

All updates are tracked using semantic versioning:

• Major versions: Significant methodology changes or new data series
• Minor versions: Data additions or minor methodology refinements
• Patch versions: Error corrections and clarifications

Change Documentation

All changes are fully documented with:

• Detailed changelog with rationale for modifications
• Impact assessment on existing analyses
• Backward compatibility notes where applicable
• Public notification of significant changes