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A Generalized Diagnostic Classification Modeling Framework Integrating Differential Speediness: Advantages and Illustrations in Psychological and Educational Testing

Abstract 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.