![]() These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.įunding: This work was supported by the National Natural Science Foundation of China (31960186 31760288 31660278 61967009). No authors reported any financial or other conflicts of interest in relation to the work described.Įthical principles: The authors affirm having followed professional ethical guidelines in preparing this work. 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.Ĭonflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. 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. Moreover, some existing models from psychometric literatures are included in the JVRT-LCDM as special cases. ![]() The JVRT-LCDM not only provides fine-grained diagnostic feedback without strict model constraints but also clarifies the specific speed trajectory of individuals. ![]() 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).
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