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- W3164926477 abstract "In this paper, we propose a model-based method to study conditional dependence be- tween response accuracy and response time (RT) with the diffusion IRT model. To this end, we extend the previously proposed model by introducing variability across persons and items in cognitive capacity and in the initial bias of the response processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. The first variability component in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. The second variability in the initial bias can account for the early changes in the response accuracy as a function of RTs given the person and item effects, producing the curvilinear conditional accuracy functions. We also provide a simulation study to validate the parameter recovery of the proposed model and two empirical applications to describe how to implement the model to study conditional dependency underlying data response accuracy and RTs." @default.
- W3164926477 created "2021-06-07" @default.
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- W3164926477 date "2021-04-10" @default.
- W3164926477 modified "2023-09-24" @default.
- W3164926477 title "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model" @default.
- W3164926477 doi "https://doi.org/10.31234/osf.io/6jv5n" @default.
- W3164926477 hasPublicationYear "2021" @default.
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