Primary Submission Category: Applicants in Social Sciences
Causal Approach for Careless Responding
Authors: Jiwoo Kim, Felix Thoemmes,
Presenting Author: Jiwoo Kim*
Careless responding has long been recognized as a threat to data quality, and methods have been developed to detect and remove potentially careless responses. However, existing approaches rarely incorporate a formal causal perspective, limiting their ability to account for the data-generating mechanisms underlying careless responses. When causal assumptions are made, they are often adopted unintentionally and may be unrealistically strong, making it implausible to identify true careless responses in practice. In addition, current definitions of careless responding do not align well with the methods used to detect it, as detection approaches classify responses based on observable patterns without linkage to the underlying causal process, creating conceptual inconsistencies. In this study, we redefine careless responding using a causal framework and reevaluate prior definitions in light of this definition. We examine how different causal mechanisms of careless responding influence bias and identifiability, using simulations that reflect distinct causal structures generating careless responses. We also propose criteria for determining when existing careless-responding methods should—or should not—be applied. By introducing a causal definition of careless responding and demonstrating its implications, this study offers a new perspective on the problem and provides a foundation for developing more principled approaches to handling careless responding in empirical research.