Predicting nonresponse to sexual identity question in youth risk behavior surveillance: A machine learning analysis of complex survey data

Key Points

• Nonresponse patterns revealed significant variation, especially among vulnerable groups.
• The predictive performance assessed was notably strong, highlighting the model's reliability.
• Machine learning techniques were employed to analyze complex survey data for identifying nonresponse trends.
• Findings emphasize the importance of survey design adjustments to account for these nonresponse issues.

Abstract