When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…

Prior research indicates that the overall reliability of performance ratings can be improved by using ordinary least squares (OLS) regression to adjust for rater effects. The present investigation extends previous work by evaluating the impact of OLS adjustment on standard errors of measurement ("SEM") at specific score levels. In…

The present study examined the long-term usefulness of estimated parameters used to adjust the scores from a performance assessment to account for differences in rater stringency. Ratings from four components of the USMLE[R] Step 2 Clinical Skills Examination data were analyzed. A generalizability-theory framework was used to examine the extent to…

Significant momentum exists for next-generation assessments to increasingly utilize technology to develop and deliver performance-based assessments. Many traditional challenges with this assessment approach still apply, including psychometric concerns related to performance-based tasks (PBTs), which include low reliability, efficiency of…

Research on developing technology for large-scale performance assessments in science is reported briefly, and a conceptual framework is presented for defining, generating, and evaluating science performance assessments. Types of tasks are discussed, and the technical qualities of performance assessments are discussed in the context of…

Results from a performance assessment in which 68 high school students wrote essays support the use of latent variable modeling for estimating reliability, concurrent validity, and generalizability of a scoring rubric. The latent variable modeling approach overcomes the limitations of certain conventional statistical techniques in handling…