Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…

Score differencing is one of six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…

According to Wollack and Schoenig (2018), score differencing is one of six types of statistical methods used to detect test fraud. In this paper, we suggested the use of Bayes factors (e.g., Kass & Raftery, 1995) for score differencing. A simulation study shows that the suggested approach performs slightly better than an existing frequentist…

For complex educational assessments, there is an increasing use of item families, which are groups of related items. Calibration or scoring in an assessment involving item families requires models that can take into account the dependence structure inherent among the items that belong to the same item family. This article extends earlier works in…

Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical…

Item families, which are groups of related items, are becoming increasingly popular in complex educational assessments. For example, in automatic item generation (AIG) systems, a test may consist of multiple items generated from each of a number of item models. Item calibration or scoring for such an assessment requires fitting models that can…

For complex educational assessments, there is an increasing use of "item families," which are groups of related items. However, calibration or scoring for such an assessment requires fitting models that take into account the dependence structure inherent among the items that belong to the same item family. C. Glas and W. van der Linden…

This paper explores the application of a technique for hierarchical item response theory (IRT) calibration of complex constructed response tasks that has promise both as a calibration tool and as a means of evaluating the isomorphic equivalence of complex constructed response tasks. Isomorphic tasks are explicitly and rigorously designed to be…

Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993)and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT…

Large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) sample examinees to whom an exam will be administered. In most situations the sampling design is not a simple random sample and must be accounted for in the estimating model. After reviewing the current operational estimation procedure for NAEP, this…