IRT Parameter Estimation [microform] : Can the Jackknife Improve Accuracy?
Unidimensional item response theory (IRT) models are routinely applied to data that are not strictly unidimensional despite the general consensus that this may lead to inaccurate parameter estimates. The jackknife technique, typically used to remove bias by re-sampling observations, may improve the accuracy of the unidimensional parameter estimates that result from multidimensional data by re-sampling items. This study examined (1) the systematic errors in marginal maximum likelihood (MML) parameter estimates that result from fitting a unidimensional two-parameter logistic IRT model to a test consisting of passage-linked groups of multiple-choice items and (2) the effectiveness of the jackknife in increasing the accuracy of these parameter estimates. Data were simulated according to one-, two-, and ten-dimensional models. Results suggest that the magnitude of bias in the unidimensional MML parameter estimates of passage-linked items depends predominantly on the characteristics of the item. Of the items classified as biased, the bias was more likely to occur in small item and person sample sizes. While the errors in the parameter estimates that arose from person sampling were fairly consistent across dimensionality models, the item sampling errors were not. Specifically, in the multidimensional conditions, the parameter estimates for a particular item varied as a result of the other items included in the calibration. The jackknife did not reduce the multidimensional item bias in the MML estimates.
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