Higher-order latent trait models for cognitive diagnosis

Jimmy De La Torre, Jeffrey A. Douglas

Research output: Contribution to journalArticlepeer-review

Abstract

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling the θ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example.

Original languageEnglish (US)
Pages (from-to)333-353
Number of pages21
JournalPsychometrika
Volume69
Issue number3
DOIs
StatePublished - Sep 2004

Keywords

  • Cognitive diagnosis
  • Item response theory
  • Latent class model
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Psychology(all)
  • Psychology (miscellaneous)
  • Social Sciences (miscellaneous)

Fingerprint

Dive into the research topics of 'Higher-order latent trait models for cognitive diagnosis'. Together they form a unique fingerprint.

Cite this