The use of predicted values for item parameters in item response theory models: An application in intelligence tests

M. Matteucci, Prof. S. Mignani, Bernard P. Veldkamp

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candidates and items might reduce the number of candidates needed for parameter estimation. Using data for IQ measurement, this work shows how empirical information about items can be used effectively for item calibration and in adaptive testing. First, we propose multivariate regression trees to predict the item parameters based on a set of covariates related to the item-solving process. Afterwards, we compare the item parameter estimation when tree-fitted values are included in the estimation or when they are ignored. Model estimation is fully Bayesian, and is conducted via Markov chain Monte Carlo methods. The results are two-fold: (a) in item calibration, it is shown that the introduction of prior information is effective with short test lengths and small sample sizes and (b) in adaptive testing, it is demonstrated that the use of the tree-fitted values instead of the estimated parameters leads to a moderate increase in the test length, but provides a considerable saving of resources.
Original languageEnglish
Pages (from-to)2665-2683
JournalJournal of applied statistics
Volume39
Issue number12
DOIs
Publication statusPublished - 2012

Keywords

  • IR-83903
  • METIS-291693

Fingerprint Dive into the research topics of 'The use of predicted values for item parameters in item response theory models: An application in intelligence tests'. Together they form a unique fingerprint.

Cite this