RESEARCH LIBRARY

Teaching and Learning in Medicine: Volume 33 - Issue 4 - p 366-381

The purpose of this analysis is to describe these sources of evidence that can be used to evaluate the quality of generated items. The important role of medical expertise in the development and evaluation of the generated items is highlighted as a crucial requirement for producing validation evidence.

Journal of Educational Measurement: Volume 58, Issue 4, Pages 515-537

In this paper, the NBME team reports the results an eye-tracking study designed to evaluate how the presence of the options in multiple-choice questions impacts the way medical students responded to questions designed to evaluate clinical reasoning. Examples of the types of data that can be extracted are presented. We then discuss the implications of these results for evaluating the validity of inferences made based on the type of items used in this study.

Educational Measurement: Issues and Practice

This short, invited manuscript focuses on the implications for certification and licensure assessment organizations as a result of the wide‐spread disruptions caused by the COVID-19 pandemic. 

Integrating Timing Considerations to Improve Testing Practices

This chapter addresses a different aspect of the use of timing data: it provides a framework for understanding how an examinee's use of time interfaces with time limits to impact both test performance and the validity of inferences made based on test scores. It focuses primarily on examinations that are administered as part of the physician licensure process.

Educational Measurement: Issues and Practice, 39: 37-44

This article presents the results of an experiment in which content experts were randomly assigned to one of two response probability conditions: .67 and .80. If the standard-setting judgments collected with the bookmark procedure are internally consistent, both conditions should produce highly similar cut scores.

Adv in Health Sci Educ 24, 141–150 (2019)

Research suggests that the three-option format is optimal for multiple choice questions (MCQs). This conclusion is supported by numerous studies showing that most distractors (i.e., incorrect answers) are selected by so few examinees that they are essentially nonfunctional. However, nearly all studies have defined a distractor as nonfunctional if it is selected by fewer than 5% of examinees.