RESEARCH LIBRARY

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: Volume 95 - Issue 1 - p 111-121

This paper investigates the effect of a change in the United States Medical Licensing Examination Step 1 timing on Step 2 Clinical Knowledge (CK) scores, the effect of lag time on Step 2 CK performance, and the relationship of incoming Medical College Admission Test (MCAT) score to Step 2 CK performance pre and post change.

Springer International Publishing; 2019

This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification.

On the Cover. Educational Measurement: Issues and Practice, 38: 5-5

This informative graphic reports between‐individual information where a vertical line—with dashed lines on either side indicating an error band—spans three graphics allowing a student to easily see their score relative to four defined performance categories and, more notably, three relevant score distributions.

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 371-377

Schools undergoing curricular reform are reconsidering the optimal timing of Step 1. This study provides a psychometric investigation of the impact on United States Medical Licensing Examination Step 1 scores of changing the timing of Step 1 from after completion of the basic science curricula to after core clerkships.

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).

Psychometrika 84, 147–163 (2019)

This paper provides results on a form of adaptive testing that is used frequently in intelligence testing. In these tests, items are presented in order of increasing difficulty. The presentation of items is adaptive in the sense that a session is discontinued once a test taker produces a certain number of incorrect responses in sequence, with subsequent (not observed) responses commonly scored as wrong.

Journal of Educational Measurement: Volume 55, Issue 4, Pages 564-581

Smoothing techniques are designed to improve the accuracy of equating functions. The main purpose of this study is to compare seven model selection strategies for choosing the smoothing parameter (C) for polynomial loglinear presmoothing and one procedure for model selection in cubic spline postsmoothing for mixed‐format pseudo tests under the random groups design.

Academic Medicine: November 2018 - Volume 93 - Issue 11S - p S21-S29

This study investigates the impact of incorporating observer-reported workload into workplace-based assessment (WBA) scores on (1) psychometric characteristics of WBA scores and (2) measuring changes in performance over time using workload-unadjusted versus workload-adjusted scores.

Journal of Graduate Medical Education: June 2018, Vol. 10, No. 3, pp. 337-338

To create examinations with scores that accurately support their intended interpretation and use in a particular setting, examination writers must clearly define what the test is intended to measure (the construct). Writers must also pay careful attention to how content is sampled, how questions are constructed, and how questions perform in their unique testing contexts.1–3 This Rip Out provides guidance for test developers to ensure that scores from MCQ examinations fit their intended purpose.