Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of…

This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were…

In this study, we introduced a cross-classified multidimensional nominal response model (CC-MNRM) to account for various response styles (RS) in the presence of cross-classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of…

Research on automated scoring is extended by comparing alternative automated systems for scoring a computer simulation of physicians' patient management skills. A regression-based system is more highly correlated with experts' evaluations than a system that uses complex rules to map performances into score levels, but both approaches are feasible.…

Empirical results from two studies--a simulation study and an experimental one--indicated that, in achievement data of the problem-solving type where a specific subject matter area is being tested, the greater the variety of the algorithms used, the higher the dimensionality of the test data. (Author/PN)

A scoring algorithm for performance assessments is described that is based on expert judgments but requires the rating of only a sample of performances. A regression-based policy capturing procedure was implemented for clinicians evaluating skills of 280 medical students. Results demonstrate the usefulness of the algorithm. (SLD)