RESEARCH LIBRARY

Psychometrika 83, 847–857 (2018)

Utilizing algorithms to generate items in educational and psychological testing is an active area of research for obvious reasons: Test items are predominantly written by humans, in most cases by content experts who represent a limited and potentially costly resource. Using algorithms instead has the appeal to provide an unlimited resource for this crucial part of assessment development.

Educational Measurement: Issues and Practice, 37: 40-45

This simulation study demonstrates that the strength of item dependencies and the location of an examination systems’ cut‐points both influence the accuracy (i.e., the sensitivity and specificity) of examinee classifications. Practical implications of these results are discussed in terms of false positive and false negative classifications of test takers.

Qual Life Res 27, 1711–1720 (2018)

The US Food and Drug Administration (FDA), as part of its regulatory mission, is charged with determining whether a clinical outcome assessment (COA) is “fit for purpose” when used in clinical trials to support drug approval and product labeling. This paper provides a review (and some commentary) on the current state of affairs in COA development/evaluation/use with a focus on one aspect: How do you know you are measuring the right thing? In the psychometric literature, this concept is referred to broadly as validity and has itself evolved over many years of research and application.