Semi-Automated Nonresponse Detection for Surveys (SANDS)
Description
Manual review of open-ended survey responses is labor-intensive and cost-prohibitive at scale. SANDS automates the detection of nonresponses in survey data, reducing the burden on researchers and improving data quality.
Detailed example
The system outputs scores for open-ended survey responses, identifying likely nonresponses and flagging responses that require further review. This helps improve survey data quality and informs questionnaire design.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
SANDS significantly reduces manual curation time for open-ended survey responses by providing automated scoring and flagging of nonresponses. This enables faster compilation of high-quality datasets for qualitative research and streamlines the review process for researchers.
Controls / human review
ATO: No; PIA: Not published
Data needed
3,000 labeled open-ended responses to web probes on questions relating to the COVID-19 pandemic, gathered from the Research and Development Survey (RANDS) conducted by the Division of Research and Methodology at the National Center for Health Statistics.