Abstract
Educational large-scale assessments provide information on ability differences between groups, informing policies and shaping educational decisions. However, some of these differences might partly reflect variations in test-taking motivation rather than in actual abilities. Existing approaches for mitigating the distorting effects of rapid guessing focus mainly on point estimates of abilities, although research questions often refer to latent variables. The present study seeks to (a) determine the bias introduced by rapid guessing in group comparisons based on plausible value estimates and (b) introduce and evaluate different approaches of handling rapid guessing in the estimation of plausible values. In a simulation study, four models were compared: (1) a baseline model did not account for rapid guessing, (2) a person-level model incorporated rapid guessing as a respondent characteristic in the background model, (3) a response-level model filtered responses with item response times lower than a predetermined threshold, and (4) a combined model merged the person- and response-level approaches. Results show that the response-level and combined model performed best while accounting for rapid guessing on the person level did not suffice. An empirical example using data from a German large-scale assessment (N = 478) demonstrates the applicability of all approaches in practice. Recommendations for future research are given to improve ability estimation.