Item Nonresponse Detection in Open-text Response Data

NCHS is developing an item nonresponse detection model, to identify cases of item nonresponse (e.g., gibberish, uncertain/don’t know, refusals, or high-risk) among open-text responses to help improve survey data and question and questionnaire design. The system is a Natural Language Processing (NLP) model pre-trained using Contrastive Learning
and fine-tuned on a custom dataset from survey responses.