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The accuracy of the Gibbs sampling Markov chain monte carlo procedure was examined for estimating item and person ( . ) parameters in the one-parameter logistic model. Four datasets were analyzed using the Gibbs sampling method, conditional maximum likelihood, marginal maximum likelihood, and...
The possibility of using surrogate variables (e.g., school grades, other test scores, examinee background information) as replacements for common items predicting sample-selection bias between groups was investigated. The problem was specified as an incomplete data problem of comparability...
The use of the generalized graded unfolding model (GGUM) in computerized adaptive testing was examined. The objective was to minimize the number of items required to produce equiprecise estimates of person locations. Simulations based on real data about college student attitudes toward abortion...
Person-fit methods based on classical test theory-and item response theory (IRT), and methods investigating particular types of response behavior on tests, are examined. Similarities and differences among person-fit methods and their advantages and disadvantages are discussed. Sound person-fit...
The number of replications in monte carlo simulation studies can be modified to improve the precision of parameter estimates. Given the speed and power of microcomputers, it is not necessary to hold the number of replications to past levels. Reasons why increasing the number of replications is...
The effects of test dimensionality (one-or three-dimensional), distribution shape (normal, positively skewed, or platykurtic), and estimation program (BILOG, MULTILOG, or XCALIBRE) on the accuracy of item and person parameter estimates were assessed. The criterion was the root mean squared error...
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