Identification of Careless Responses and Their Patterns in Low-Stake Assessment

• In Event: AERA Poster Session Wednesday 7:45am
  In Poster Session: Insights from Large-Scale Assessments: Advancing Methods, Measurement, and Learning Opportunities

Abstract

This study aims to detect careless responses in low-stakes assessments and identify latent patterns of such behavior. Using data from the PISA 2022 Science assessment, we applied a dependent latent class item response theory (DLC-IRT) model that incorporates item response times to classify responses as valid or careless. Based on these classifications, a latent class analysis (LCA) was conducted to explore patterns of careless responding across examinees. Results revealed three distinct response profiles: consistently engaged, partially careless, and generally careless groups. These findings highlight the non-random nature of disengaged behavior and underscore the importance of accounting for response quality when interpreting assessment results, particularly in large-scale, low-stakes testing contexts.

Authors

• Meereem Kim, Korea Institute for Curriculum and Evaluation

• Yoonsun Jang, Daegu National University of Education