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
Expanded data unlocks statistical power and demographic completeness from your existing sample investment.
Increase Statistical Power
Larger samples provide more reliable insights, narrower confidence intervals, and greater ability to detect meaningful differences across subgroups.
Fill Demographic Quotas
Add specific demographic segments you're missing to achieve balanced representation and meet quota requirements without re-fielding.
Match Existing Patterns
New respondents align with your current data distribution, preserving the statistical characteristics and relationships in your original sample.
No Additional Fielding
Avoid costly and time-consuming re-recruitment. Expand your dataset in minutes rather than weeks, at a fraction of the cost.
Preserve Data Integrity
Maintain the statistical characteristics of your original sample. Expanded respondents reflect the same correlations, distributions, and response patterns.
Perfect For
- Small sample sizes needing a power boost
- Incomplete demographic quotas that need filling
- Rare population segments that are difficult or expensive to recruit
- Pilot studies requiring scale-up for statistical significance
- Academic research with limited budgets
- Time-sensitive analysis needs where re-fielding is not an option
Quality Assurance
Every expanded dataset undergoes rigorous quality checks to ensure statistical fidelity.
- Statistical pattern preservation: Response distributions in the expanded data match the original sample
- Demographic distribution matching: Expanded segments reflect the characteristics of their source subgroups
- Response consistency validation: Each synthetic respondent is checked for internal coherence across questions
- Correlation structure maintenance: Cross-variable relationships in the original data are preserved in the expansion
- Outlier and edge case inclusion: Natural variability is maintained — expanded data is not artificially smooth
- Cross-variable relationship integrity: Interactions between demographics and responses remain statistically consistent
How It Works
A straightforward five-step process from upload to delivery.
- Upload Your Original Data: Provide your existing survey results in CSV, SPSS, or Excel format. The system ingests your respondent-level data along with demographic variables.
- Define Expansion Needs: Specify your target sample size and demographic requirements. Select which subgroups need expansion and by how many respondents.
- Pattern Analysis: AI models learn your data's response patterns and distributions. The system identifies correlations, skip logic, and response structures specific to your survey.
- Generate Matching Respondents: Create new synthetic respondents that fit your sample profile. Each generated respondent is validated against your original data's statistical signature.
- Download Combined Dataset: Get your original plus expanded data in standard formats. The combined dataset includes a flag column identifying original versus expanded respondents.
Combined output: The expanded dataset maintains the statistical properties of your original sample while providing the larger size needed for robust analysis. Original respondents are always preserved exactly as-is — expansion only adds, never modifies.
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.
Pilot Study Expansion
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