Incorporating Nonprobability Data to Expand Web-Based Health Survey Estimates

Thomas M. Menino Convention & Exhibition Center 

Nonprobability web-based data sources, such as opt-in panels and convenience samples, have become increasingly available and new data can be collected at a low cost. However, nonprobability data may suffer from bias as they lack a probability sampling structure and may not apply the same level of quality controls used for probability-based data collections. While there are concerns about the reliability of estimates obtained from nonprobability data sources, statistical approaches to integrate nonprobability data with probability surveys offer opportunities to expand the scope of estimates, particularly among small subpopulations, as well as the precision of those estimates.

This presentation considers an application from the Research and Development Survey (RANDS), a web-based health survey conducted by the National Center of Health Statistics. Recent rounds of RANDS have included both probability-based and nonprobability components, with identical surveys administered to each set of respondents. Methodological strategies for combining data from the probability and nonprobability samples to improve and expand health estimates for small domains are explored and empirical findings are used to demonstrate how these blended approaches can reduce bias and improve efficiency.