Misclassification Produced by Rapid-Guessing Identification Methods and Their Suitability Under Various Conditions

Abstract

Response Time Threshold Methods (RTTMs) are widely used to identify rapid-guessing behavior (RG) in low-stakes assessments, yet face two key challenges: (a) inevitable misclassifications due to overlapping response time distributions of engaged and disengaged responses, and (b) lack of agreement on which method to use under varying conditions. This simulation study evaluated five RTTMs. Item responses and response times were generated from either a one-component model without RG or a two-component mixture model with RG in the population. Distribution, item, and person parameters were varied. Results showed that when the population contained RG, the mixture lognormal distribution-based method (MLN) was the most robust approach and estimated precise thresholds closest to the time points at which the misclassification rates were minimized, even when bimodality was more difficult to detect. The cumulative proportion method (CUMP) was less robust but also accurate when successful, though less precise. In addition, when the population did not include RG, CUMP was the only method to set thresholds for a notable proportion of cases. The methods were generally more conservative than liberal, though the mixture response time quantile method (MRTQ) was neither. The results are discussed in the light of prior RG research and the methods' characteristics, and future directions are suggested. Ultimately, for practical settings, we recommend a six-step process for RG identification that utilizes both a mixture modeling approach (MLN or MRTQ) and the CUMP method.