Expanded Data

Boost Sample Sizes

Expand small samples and fill demographic gaps with synthetic respondents that match your existing data patterns. Increase statistical power and achieve quota targets without additional fielding costs.

What is Expanded Data?

Expanded data generation takes your existing survey results and creates additional synthetic respondents that match your sample's response patterns and demographic characteristics. This allows you to increase sample sizes, fill demographic quotas, and improve statistical power without the time and cost of additional fielding.

Important: How Subgroup Expansion Works

Expansion works by extending existing subgroups in your data—it does not create entirely new demographic segments. For example, to expand females aged 25-34, you need an existing subsample of females aged 25-34 in your original data that serves as "context" for generating additional respondents with those same characteristics.

Key Benefits

  • Increase Statistical Power: Larger samples provide more reliable insights
  • Fill Demographic Quotas: Add specific demographic segments you're missing
  • Match Existing Patterns: New respondents align with your current data distribution
  • No Additional Fielding: Avoid costly and time-consuming re-recruitment
  • Preserve Data Integrity: Maintain the statistical characteristics of your original sample

Perfect For

  • Small sample sizes needing power boost
  • Incomplete demographic quotas
  • Rare population segments
  • Pilot studies requiring scale-up
  • Academic research with limited budgets
  • Time-sensitive analysis needs

Quality Assurance

  • Statistical pattern preservation
  • Demographic distribution matching
  • Response consistency validation
  • Correlation structure maintenance
  • Outlier and edge case inclusion
  • Cross-variable relationship integrity

How It Works

  1. Upload Your Original Data: Provide your existing survey results
  2. Define Expansion Needs: Specify target sample size and demographic requirements
  3. Pattern Analysis: AI models learn your data's response patterns and distributions
  4. Generate Matching Respondents: Create new synthetic respondents that fit your sample profile
  5. Download Combined Dataset: Get original + expanded data in standard formats

The expanded dataset maintains the statistical properties of your original sample while providing the larger size needed for robust analysis.

Example Use Cases

Demographic Quota Filling

Challenge: 400-person study missing 18-24 age group (only 12 respondents)

Solution: Generate 38 additional 18-24 respondents matching your sample patterns

Result: Balanced 450-person dataset with proper age representation

Challenge: 150-person pilot study needs 500+ for statistical significance

Solution: Expand to 600 respondents maintaining original characteristics

Result: Statistically powered dataset ready for publication or decision-making

Technical Specifications

  • Demographic Matching: Age, gender, income, education, location
  • Pattern Preservation: Statistical correlations and distributions maintained
  • File Integration: Seamless combination with original data
  • Quality Validation: Automated checks for distribution accuracy