Modeling rule-based item generation

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)

Abstract

An application of a hierarchical IRT model for items in families generated through the application of different combinations of design rules is discussed. Within the families, the items are assumed to differ only in surface features. The parameters of the model are estimated in a Bayesian framework, using a data-augmented Gibbs sampler. An obvious application of the model is computerized algorithmic item generation. Such algorithms have the potential to increase the cost-effectiveness of item generation as well as the flexibility of item administration. The model is applied to data from a non-verbal intelligence test created using design rules. In addition, results from a simulation study conducted to evaluate parameter recovery are presented.
Original languageEnglish
Pages (from-to)337-359
JournalPsychometrika
Volume76
Issue number2
DOIs
Publication statusPublished - 2011

Keywords

  • item generation
  • IR-85853
  • METIS-282123
  • Hierarchical modeling
  • Item Response Theory
  • Markov chain Monte Carlo method

Fingerprint

Dive into the research topics of 'Modeling rule-based item generation'. Together they form a unique fingerprint.

Cite this