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Browsing by Author "Oshima, T. C."

Now showing 1 - 3 of 3
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    Effect of sample size, number of biased items, and magnitude of bias on a two-stage item bias estimation method
    (1992) Miller, M. David; Oshima, T. C.
    A two-stage procedure for estimating item bias was examined with six indexes of item bias and with the Mantel-Haenszel (MH) statistic; the sample size, the number of biased items, and the magnitude of the bias were varied. The second stage of the procedure did not identify substantial numbers of false positives (unbiased items identified as biased). However, the identification of true positives in the second stage was useful only when the magnitude of the bias was not small and the number of biased items was large (20% or 40% of the test). The weighted indexes tended to identify more true and false positives than their unweighted item response theory counterparts. Finally, the MH statistic identified fewer false positives, but did not identify small bias as well as the item response theory indexes. Index terms: differential item functioning, item bias, Mantel-Haenszel statistic, two-stage bias estimation.
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    Linking multidimensional item calibrations
    (1996) Davey, Tim; Oshima, T. C.; Lee, Kevin
    Invariance of trait scales across changing samples of items and examinees is a central tenet of item response theory (IRT). However, scales defined by most IRT models are truly invariant with respect to certain linear transformations of the parameters. The problem is to find the proper transformation that places separate calibrations on a common scale. A variety of procedures for estimating transformations have been proposed for unidimensional models. This paper explores some issues involved in extending and adapting unidimensional linking procedures for use with multidimensional IRT models. Index terms: equating, item response theory, linking, metric in IRT, multidimensional IRT, scale linking.
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    Multidimensionality and item bias in item response theory
    (1992) Oshima, T. C.; Miller, M. David
    This paper demonstrates empirically how item bias indexes based on item response theory (IRT) identify bias that results from multidimensionality. When a test is multidimensional (MD) with a primary trait and a nuisance trait that affects a small portion of the test, item bias is defined as a mean difference on the nuisance trait between two groups. Results from a simulation study showed that although IRT-based bias indexes clearly distinguished multidimensionality from item bias, even with the presence of a between-group difference on the primary trait, the bias detection rate depended on the degree to which the item measured the nuisance trait, the values of MD discrimination, and the number of MD items. It was speculated that bias defined from the MD perspective was more likely to be detected when the test data met the essential unidimensionality assumption. Index terms: item bias, multidimensionality; item response theory, item bias, mean differences, multidimensionality; multidimensionality; mean differences in IRT.