RESEARCH LIBRARY

Applied Psychological Measurement: Volume 46, issue 2, page(s) 571-588

This study evaluates the degree to which position effects on two separate low-stakes tests administered to two different samples were moderated by different item (item length, number of response options, mental taxation, and graphic) and examinee (effort, change in effort, and gender) variables. Items exhibited significant negative linear position effects on both tests, with the magnitude of the position effects varying from item to item.

Applied Psychological Measurement: Volume 46, issue 6, page(s) 529-547

The current simulation study demonstrated that the sampling variance associated with the item response theory (IRT) item parameter estimates can help detect outliers in the common items under the 2-PL and 3-PL IRT models. The results showed the proposed sampling variance statistic (SV) outperformed the traditional displacement method with cutoff values of 0.3 and 0.5 along a variety of evaluation criteria.

Journal of Educational Measurement: Volume 59, Issue 2, Pages 140-160

A conceptual framework for thinking about the problem of score comparability is given followed by a description of three classes of connectives. Examples from the history of innovations in testing are given for each class.

Educational Measurement: Issues and Practice

This article aims to answer the question: when the assumption that examinees may apply themselves fully yet still respond incorrectly is violated, what are the consequences of using the modified model proposed by Lewis and his colleagues? 

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.

Academic Medicine: July 2019 - Volume 94 - Issue 7 - p 926-927

A response to concerns regarding potential bias in the implementation of machine learning (ML) to scoring of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) patient notes (PN).

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 314-316

The United States Medical Licensing Examination Step 2 Clinical Skills (CS) exam uses physician raters to evaluate patient notes written by examinees. In this Invited Commentary, the authors describe the ways in which the Step 2 CS exam could benefit from adopting a computer-assisted scoring approach that combines physician raters’ judgments with computer-generated scores based on natural language processing (NLP).

Journal of Medical Regulation (2018) 104 (2): 51–57

There have been a number of important stakeholder opinions critical of the Step 2 Clinical Skills Examination (CS) in the United States Medical Licensing Examination (USMLE) licensure sequence. The Resident Program Director (RPD) Awareness survey was convened to gauge perceptions of current and potential Step 2 CS use, attitudes towards the importance of residents' clinical skills, and awareness of a medical student petition against Step 2 CS. This was a cross-sectional survey which resulted in 205 responses from a representative sampling of RPDs across various specialties, regions and program sizes.