Showing 1 - 4 of 4 Research Library Publications
Posted: | Ian Micir, Kimberly Swygert, Jean D'Angelo

Journal of Applied Technology: Volume 23 - Special Issue 1 - Pages 30-40

 

The interpretations of test scores in secure, high-stakes environments are dependent on several assumptions, one of which is that examinee responses to items are independent and no enemy items are included on the same forms. This paper documents the development and implementation of a C#-based application that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to produce prioritized predictions of item enemy statuses within a large item bank.

Posted: | B. E. Clauser, M. Kane, J. C. Clauser

Journal of Educational Measurement: Volume 57, Issue 2, Pages 216-229

 

This article presents two generalizability-theory–based analyses of the proportion of the item variance that contributes to error in the cut score. For one approach, variance components are estimated on the probability (or proportion-correct) scale of the Angoff judgments, and for the other, the judgments are transferred to the theta scale of an item response theory model before estimating the variance components.

Posted: | Z. Jiang, M.R. Raymond

Applied Psychological Measurement: Volume: 42 issue: 8, page(s): 595-612

 

Conventional methods for evaluating the utility of subscores rely on reliability and correlation coefficients. However, correlations can overlook a notable source of variability: variation in subtest means/difficulties. Brennan introduced a reliability index for score profiles based on multivariate generalizability theory, designated as G, which is sensitive to variation in subtest difficulty. However, there has been little, if any, research evaluating the properties of this index. A series of simulation experiments, as well as analyses of real data, were conducted to investigate G under various conditions of subtest reliability, subtest correlations, and variability in subtest means.

Posted: | K. Walsh, P. Harik, K. Mazor, D. Perfetto, M. Anatchkova, C. Biggins, J. Wagner

Medical Care: April 2017 - Volume 55 - Issue 4 - p 436-441

 

The objective of this study is to identify modifiable factors that improve the reliability of ratings of severity of health care–associated harm in clinical practice improvement and research.