A Generative AI Approach for Integrating Synthetic Respondents with Probability-Based Human Panels

Thomas M. Menino Convention & Exhibition Center 

The growing demand for rapid, cost-effective, and scalable research solutions is driving interest in synthetic data for social science research. Synthetic respondents offer efficiency and scalability while reducing respondent burden, but their use must preserve data quality. This paper introduces a novel approach combining probability-based samples with synthetic respondents designed to mirror human respondents. Leveraging generative AI, we fine-tune large language models on NORC's AmeriSpeak panel data to create realistic synthetic panelists that emulate both aggregate response patterns and nuanced respondent behaviors. We compare finetuning strategies against context engineering-only approaches to optimize predictive validity. Synthetic responses are integrated with human data using dynamic models that adjust based upon predictive accuracy, ensuring insights remain grounded in authentic responses. We additionally implement ongoing validation protocols to assess bias, variance, and representativeness. We present findings from a pilot study, including comparative analyses of methods, integration strategies, and validation outcomes, and note implications for survey methodology.

Synthetic data

Survey methodology

Probability-based samples

Large language models

Data integration and fusion 

Survey Research Methods Section