389 – Improving Survey Data Quality with Machine Learning Techniques
Artificial Intelligence (AI)-Enhanced Applications to Survey-Specific Imputation Tasks to Achieve Time and Cost Efficiencies
Steven B. Cohen
RTI International
Jamie Shorey
RTI International
A high degree of rigor is essential in the statistical integrity of "end-product" analytic resources that are used to inform policy and action. In this vein, statistical and analytic staff devote substantial time and effort to implement estimation and imputation tasks; these tasks are essential components of the "end-product" analytic databases derived from national or sub-national surveys and related data collections. These efforts require a substantial commitment of project related funds to achieve, and significant lag times often exist from the time data collection is completed to the time the final analytical data file is released. This presentation focuses imputation methodology enhanced with artificial intelligence (AI) for specific national survey efforts. We demonstrate the efficiencies achieved by the AI-enhanced applications in terms of cost and time that satisfy well-defined levels of accuracy to ensure data integrity. Attention is given to AI-enhanced processes thatserve as an alternative solution to manual, repetitive or time-intensive tasks. Examples are provided with applications to national survey efforts that include the Medical Expenditure Panel Survey.