Beyond One-Size-Fits-All: A Differential Sensitivity Framework for Machine Learning–Based Detection of Anomalous Survey Responses
Educational and Psychological Measurement
Published online on June 01, 2026
Abstract
Educational and Psychological Measurement, Ahead of Print.
Anomalous survey responses, including random, careless, extreme, acquiescent, straightline, and alternating responding, threaten the validity of survey-based research. Machine learning (ML) algorithms offer flexible, model-agnostic alternatives to ...
Anomalous survey responses, including random, careless, extreme, acquiescent, straightline, and alternating responding, threaten the validity of survey-based research. Machine learning (ML) algorithms offer flexible, model-agnostic alternatives to ...