LAUSR

To advance the theoretical foundation of incorporating response times (RTs) into diagnostic classification models (DCMs), this study attempts to further derive, test and illustrate a generalized modeling framework (known as the JVRT-LCDM) that can simultaneously analyze response accuracy and differential speediness based on an existing method (Zhan et al., British Journal of Mathematical and Statistical Psychology, 71(2), 262-286, 2018). The JVRT-LCDM not only provides fine-grained diagnostic feedback without strict model constraints but also clarifies the specific speed trajectory of individuals. Moreover, some existing models from psychometric literatures are included in the JVRT-LCDM as special cases. The feasibility of the JVRT-LCDM is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme, and two empirical datasets are then analyzed to illustrate the applicability of the JVRT-LCDM in practice. The results indicate that (1) as a generalized and flexible model, the JVRT-LCDM realizes high correct classification rates and accurate speed parameter recovery; (2) the JVRT-LCDM outperforms the existing models in terms of model-data fit, diagnostic consistency, and estimation of specific individuals in practical cognitive diagnosis assessments; and (3) the JVRT-LCDM provides reliable evidence for nonconstant speed modeling.