Measuring People and Thoughtfully Representing Them When Interpreting Statistical Models

Scarlett Bellamy Speaker
Boston University School of Public Health
 
Tuesday, Aug 8: 11:25 AM - 11:50 AM
Invited Paper Session 
Metro Toronto Convention Centre 
Although the mere mention of critical race theory (CRT) can elicit strong feelings and has become one of the most divisive concepts in the United States, a recent University of Southern California study estimated that nearly 50% of indicated not knowing much about CRT or had never heard of the term at all. With this as the backdrop, many have called into question whether CRT can be measured quantitatively. Similarly, a recent commentary Robinson, Renson and Naimi (2020) argued that persons conducting machine learning analyses in settings involving human health should be literate in structural racism and further assert that this skill is essential in any research involving human health. Here, I present a review of recent work to demonstrate how CRT has been operationalized in various scientific contexts, including illustrating how different terms, constructs and concepts have been used interchangeably in the literature and in other outlets. Additionally, I will present principles for thoughtfully characterizing people and how we can approach thoughtfully representing them more holistically when interpreting statistical models.