 Approximations of Normal IRT Models for ChangeElaine Tan (), A.W. Ambergen, R.J.M.M. Does and Tj. Imbos Journal of Educational and Behavioral Statistics, 1999, vol. 24, issue 2, 208-223 Abstract: In this paper the one parameter Item Response Theory (IRT) model with normal Item Characteristic Curves (ICC) in longitudinal context has been studied. The abilities are structured according to a general mixed effects linear regression model. The items are supposed to be a sample from a large bank of items with constant mean difficulty. If the number of repeated measures is large, then commonly used simultaneous estimation procedures often lead to practical problems with respect to multidimensional numerical integrations. In this article, an approximation of the normal ICC is introduced that leads to simple ability and difficulty estimators with nice asymptotic properties. The relative efficiency and bias of the ability estimator are studied. An illustration with real data shows high relative efficiency within an acceptable range of the domain of the ICC. Moreover, the bias is very small. A simulation study shows the effect of non-normal item parameters on the regression estimates. The results suggest that the proposed procedure is rather robust against departures from normality. However, the estimation of the correlations between regression parameters can be seriously biased. Keywords: closed form estimators; IRT; longitudinal (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:24:y:1999:i:2:p:208-223 DOI: 10.3102/10769986024002208 Access Statistics for this article More articles in Journal of Educational and Behavioral Statistics |
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