Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

Inhan Kang, Paul De Boeck, Roger Ratcliff

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


In this paper, we propose a model-based method to study conditional dependence between response accuracy and response time (RT) with the diffusion IRT model (Tuerlinckx and De Boeck in Psychometrika 70(4):629–650, 2005,; van der Maas et al. in Psychol Rev 118(2):339–356, 2011, We extend the earlier diffusion IRT model by introducing variability across persons and items in cognitive capacity (drift rate in the evidence accumulation process) and variability in the starting point of the decision processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. Variability in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. Variability in starting point can account for the early changes in the response accuracy as a function of RT given the person and item effects. By the combination of the two variability components, the extended model can produce the curvilinear conditional accuracy functions that have been observed in psychometric data. We also provide a simulation study to validate the parameter recovery of the proposed model and present two empirical applications to show how to implement the model to study conditional dependency underlying data response accuracy and RTs.

Original languageEnglish
Pages (from-to)725-748
Number of pages24
Issue number2
Publication statusPublished - 2022 Jun

Bibliographical note

Publisher Copyright:
© 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics


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