Devising a Principled Rapid Guessing Workflow: Identification, Impact, and Treatment of Rapid Guessing in Cognitive Assessment

Abstract

Low-stakes assessments are particularly vulnerable to rapid guessing behavior, where responses may reflect minimal cognitive engagement, potentially biasing scores and weakening inferences. Drawing on a scoping review of more than 70 studies, we summarize how rapid guessing is identified, its impact on key parameters, and how it is treated. Detection methods range from time-based thresholds to statistical models using response-time or residual data, each balancing practicality and precision. Treatments include filtering, downweighting, and model-based adjustments, with differing consequences for score meaning and comparability. However, decisions about identifying rapid guessing, evaluating its impact, and selecting treatments are often not explicitly linked, making it difficult to arrive at coherent analytic choices. To address this, we propose a four-step workflow and reporting guidelines: (1) clarify construct relevance, (2) screen using context-appropriate methods, (3) assess impact via sensitivity analysis, and (4) justify a treatment. Together, these tools provide a practical framework for defensible educational measurement practice.