A causal approach to careless responding

Careless responding has long been recognized as a threat to data quality, and numerous methods have been developed to detect and remove potentially careless responses. However, existing approaches rarely adopt a formal causal perspective, limiting their ability to distinguish the mechanisms that generate careless responses from the processes used to detect and handle them. In this study, we develop a framework for careless responding that explicitly distinguishes three stages: response generation, detection, and handling. At the generation stage, we use causal graphical models to show how attentive and careless responses combine to produce the observed data. We then introduce a typology of careless responding mechanisms and examine how these mechanisms affect bias and identifiability. At the detection stage, we characterize detection procedures as imperfect attempts to infer latent response states from observed response patterns. At the handling stage, we show that the validity of deletion or adjustment depends jointly on the underlying response-generating mechanism and the accuracy of detection. Through a series of targeted simulations, we demonstrate how bias can arise from misalignment between the generating mechanism and the handling strategy, as well as from imperfect classification. By integrating these stages within a unified framework, this study clarifies why commonly used approaches may fail and provides a foundation for developing more principled strategies for addressing careless responding in empirical research.