Correcting Bias in Political Polling Using Synthetic Data

Client

A leading national polling institute preparing for an election cycle

Challenge

The client conducted a large-scale survey to measure candidate preferences across the country. However, post-fieldwork review revealed two major issues:

• Elderly voters (65+) were underrepresented, making up just 11% of the sample, compared to 24% in the national census
• Urban dwellers were overrepresented, especially from major metro areas
• Given that older and rural voters tend to vote differently, this imbalance threatened to skew the reported results

The client wanted a way to understand and correct for this bias—without running a new, expensive round of fieldwork.

Solution

The client engaged Correlix, DataDiggers’ synthetic data engine, to simulate an adjusted dataset that would reflect the true population structure, based on official census benchmarks.

Using the original survey as input, Correlix generated 50,000 synthetic records that:

• Increased representation of 65+ respondents to match census data
• Rebalanced urban/rural split (from 72/28 to a more accurate 55/45)
• Preserved real-world voting patterns—ensuring that age, geography, and political preference still followed observed correlations
• Delivered a clear, auditable logic sheet and optional summary visuals

All of this was achieved using Correlix’s built-in Gaussian Copula-based logic engine, without accessing any new personal data, and with full GDPR compliance.

How It Worked

Step 1: Input Review
Client provided the original dataset (800 completes), with key demographics and preference variables.

Step 2: Logic Instructions
The Correlix request form specified:

• Match census distribution for age and urban/rural status
• Preserve relationship between demographics and party choice
• Generate 50,000 synthetic respondents for comparative modeling

Step 3: Generation & Review
Correlix’s statistical engine produced the dataset in 5 business days, along with:

• A logic summary sheet explaining how variables were modeled
• A synthetic dataset in Excel
• (Optional) Charts comparing original vs corrected results

Outcome

The client used the synthetic dataset to compare original and corrected candidate scores. The differences were significant:

• Candidate A’s support dropped by 4.2% once rural and elderly voters were properly weighted
• Candidate B’s support rose by 3.7% among 65+ voters
• The client revised its forecasting model using the corrected figures

Value Delivered

✅ Uncovered hidden voting trends that would have been missed due to sampling gaps
✅ Improved forecasting accuracy for campaign strategy
✅ Avoided costly re-fielding
✅ Enabled transparent bias correction for stakeholders and media reporting

Why Correlix?

• Statistical integrity: Patterns observed in real data are preserved
• Privacy-first: No re-identification or personal data exposure
• Rapid delivery: 50,000 records in 5 days
• Custom-fit logic: Client-defined quotas and filters built in
• Full support: From request setup to interpretation

Want to See How It Works?

Whether you’re polling voters, planning a campaign, or adjusting for low-incidence segments, Correlix gives you credible, scalable, and bias-corrected insights without the wait or cost of new fieldwork.

Request a demo or contact our team to explore how Correlix can support your next election study.