Detecting Careless Responding in Survey Data Using Stochastic Gradient Boosting.

ABSTRACT: Careless responding is a bias in survey responses that disregards the actual item content, constituting a threat to the factor structure, reliability, and validity of psychological measurements. Different approaches have been proposed to detect aberrant responses such as probing questions that directly assess test-taking behavior (e.g., bogus items), auxiliary or paradata (e.g., response times), or data-driven statistical techniques (e.g., Mahalanobis distance). In the present study, gradient boosted trees, a state-of-the-art machine learning technique, are introduced to identify careless respondents. The performance of the approach was compared with established techniques previously described in the literature (e.g., statistical outlier methods, consistency analyses, and response pattern

SUBMITTER: Schroeders U 

PROVIDER: S-EPMC8725053 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature