An joint maximum likelihood estimation approach to cognitive diagnosis models

Youn Seon Lim, Fritz Drasgow

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this study, a simulation-based method for computing joint maximum likelihood estimates of cognitive diagnosis model parameters is proposed. The central theme of the approach is to reduce the complexity of models to focus on their most critical elements. In particular, an approach analogous to joint maximum likelihood estimation is taken, and the latent attribute vectors are regarded as structural parameters, not parameters to be removed by integration with this approach, the joint distribution of the latent attributes does not have to be specified, which reduces the number of parameters in the model. The Markov Chain Monte Carlo algorithm is used to simultaneously evaluate and optimize the likelihood function. This streamlined approach performed as well as more traditional methods for models such as the DINA, and affords the opportunity to fit more complicated models in which other methods may not be feasible.

Original languageEnglish (US)
Title of host publicationQuantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017
EditorsJorge Gonzalez, Rianne Janssen, Marie Wiberg, Dylan Molenaar, Steven Culpepper
Number of pages16
ISBN (Print)9783319772486
StatePublished - 2018
Event82nd Annual meeting of the Psychometric Society, 2017 - Zurich, Switzerland
Duration: Jul 17 2017Jul 21 2017

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Other82nd Annual meeting of the Psychometric Society, 2017


  • Cognitive diagnosis model
  • Joint maximum likelihood estimation
  • Simulated annealing

ASJC Scopus subject areas

  • General Mathematics


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