Our study is mindful of STEM disparities and health inequities that exist for populations.

In order to measure those inequities, we need better ways of measuring populations who may be most at risk. New research has highlighted that we need to expand wording of demographic questions to be inclusive. Likewise, there are new questions that we should add to better understand underrepresented populations within STEM.

Explore demographics sections below to learn about our questions and their scientific rationale.

Study Demographics

Basic Demographics

These are questions that everyone in our anonymous study receives.

Age, gender identity, race and ethnicity.

Learn more about basic demographics.

Expanded Demographics

These are questions that may be added to any data collection event to support a better understanding of STEM development among these populations.

Disability, language, sexual orientation (identity), disadvantaged background.

Learn more about expanded demographics.

Specific Populations

Sexual Orientation and Gender Identity (SOGI)

We ask about gender identity to all participants in basic demographics. Sexual orientation is included in expanded demographics.

Learn more about

gender identity

sexual orientation

Race & Ethnicity

We use expanded options to ask about race and ethnicity, using phrasing from Oregon's Race, Ethnicity, Language, and Disability (REALD) instrument.

Learn more about race and ethnicity


Some experience health and service differences that can influence access. We use Oregon's Race, Ethnicity, Language, and Disability (REALD) instrument.

Learn more about disability

Underrepresented Populations

National Institutes of Health defines populations who are underrepresented in STEM and biomedical fields.

Learn more about underrepresented populations

Health Inequities applied to STEM

We use Race, Ethnicity, Language, and Disability (REALD) demographic data collection standards. As beautifully described by McGee and colleagues (2020):

"Demographic data matter because certain groups of people experience avoidable health inequities. Everyone does not:

• Receive the same level of health care, and

• Have the same access to quality health care.

This results in avoidable differences in health outcomes. Avoidable differences in health due to race, ethnicity, language, and disability have been clearly documented. However, we have not been able to fully address and eliminate these inequities. In order to accurately identify health inequities and subpopulations that may benefit from focused interventions, data collection with more granularity in race, ethnicity and language (Hasnain-Wynia et al., 2007; Institute of Medicine (IOM), 2003; Ulmer, McFadden, & Nerenz, 2009) is needed. Additionally, there is a need for data collection of disability as a demographic. This helps to fully identify and address avoidable health inequities experienced by people with disabilities (Krahn, Walker, & Correa-De-Araujo, 2015; Wisdom et al., 2010)."

***McGee, M.G. (2020). Race, ethnicity, language and disability (REALD) implementation guide. Portland, Oregon: Oregon Health Authority, Equity and Inclusion Division. page 9 (REALD site; link to PDF)

We believe these same principles can and should be applied to evaluation of students' STEM development.