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

Matteucci, Mariagiulia ; Mignani, Stefania ; Veldkamp, Bernard P. (2012) The use of predicted values for item parameters in item response theory models: an application in intelligence tests. [Preprint]
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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, 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.

Abstract
Document type
Preprint
Creators
CreatorsAffiliationORCID
Matteucci, Mariagiulia
Mignani, Stefania
Veldkamp, Bernard P.
Keywords
item response theory models, Bayesian estimation, multivariate regression trees, item calibration, adaptive testing, intelligence tests.
Subjects
DOI
Deposit date
13 Mar 2012 09:20
Last modified
05 Apr 2012 09:54
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