There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate.…

In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic…

This article describes two separate, related studies that provide insight into the effectiveness of "e-rater" score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of "e-rater" scoring model that was cost-effective and applicable under conditions of absent human rating and…

Standard setting theory has largely developed with reference to a typical situation, determining a level or levels of performance for one exam for one context. However, standard setting is now being used with international reference frameworks, where some parameters and assumptions of classical standard setting do not hold. We consider the…

"Item models" (LaDuca, Staples, Templeton, & Holzman, 1986) are classes from which it is possible to generate items that are equivalent/isomorphic to other items from the same model (e.g., Bejar, 1996, 2002). They have the potential to produce large numbers of high-quality items at reduced cost. This article introduces data from an…

Item response theory (IRT) has become one of the most popular scoring frameworks for measurement data. IRT models are used frequently in computerized adaptive testing, cognitively diagnostic assessment, and test equating. This article reviews two of the most popular software packages for IRT model estimation, BILOG-MG (Zimowski, Muraki, Mislevy, &…