Test speededness, caused by time constraints, can impact examinees' performance, leading to decreased response accuracy, particularly toward the end of the test.Most existing methods for detecting test speededness rely on specific distributional assumptions for response times (RTs), such as the lognormal distribution, which may lead to incorrect statistical inference if the true data distribution deviates from these assumptions.This paper proposes a novel Bootstrap-CUSUM method for detecting test speededness, which is robust to non-normality in log-RTs.By constructing a CUSUM person-fit statistic for log-RTs and using the multiplier bootstrap to estimate its empirical distribution, our method facilitates individual level detection and changepoint estimation.We prove the theoretical consistency of the method under both null and alternative hypotheses.Simulation studies show that the Bootstrap-CUSUM method outperforms the likelihood ratio test, Wald test, and score test in terms of correct classification rate, true detection rate, and false positive rate, demonstrating superior robustness and adaptability across different data distributions.The real data analysis further demonstrates the practical utility of the proposed method for detecting test speededness.
Chang et al. (Mon,) studied this question.