Back To Projects

What Factors Correlate with the Relationship Between Gender and Race and Pursuing STEM?

Saket R.

In this paper, I sought to answer the question, “How do races and genders differ in the way they pursue STEM, and what factors correlate with these differences?” Identifying which factors cause this lack of representation is the most important step in fixing the diversity problem.


The world of technology and Science, Technology, Engineering, and Math (STEM) is dominated by Caucasians, Asians, and males. According to Computer Science.org, while African Americans and Latinos make up 13.4% and 16% of the US population respectively, they only comprise 9% and 7% of STEM occupations (McGee). In this paper, I sought to answer the question, “How do races and genders differ in the way they pursue STEM, and what factors correlate with these differences?” Identifying which factors cause this lack of representation is the most important step in fixing the diversity problem. To answer this question, I used data from the High School Longitudinal Study (HSLS), which asked hundreds of questions at different points throughout students’ high school and college careers. I used 16 of these questions and analyzed how strongly they correlate with pursuing a STEM career or not pursuing a STEM career. I found that Asians are much more likely to pursue STEM, African Americans are much less likely to pursue STEM, and women are much more likely to pursue STEM, although they largely pursued health occupations within STEM. It is important that we make sure everyone feels comfortable pursuing or not pursuing STEM. From my literature review, I found that Asians are more likely to and African Americans are less likely to pursue STEM because Asians feel like they fit in and African Americans feel like they don’t (Milgrom-Elcott). In order to increase the diversity in the STEM workforce, we need to make sure all minority groups feel welcomed in high school STEM classes, college STEM courses, and STEM workplaces themselves.

Explore More!

Saket R.
Bradley Yam
Computer Science BS from Yale, Software Engineer

Related Projects

The Motions of Forest Fires

Can I make an AI model that can predict how forest fires will move? I am trying to take data about the environment and a current fire and predict where the fire will go / if it will grow or shrink.
Giuliano T.
Mentored by Sophia Barton
The Impact of the Covid-19 Pandemic on the Test Scores of Various Demographics

The research problem that this research paper attempts to address is, How did Covid impact and affect the test scores of different demographics of students in various schools in New York? The overall approach that was used to answer this question involved using statistical analysis to examine the correlation between how certain factors such as being economically disadvantaged or being hispanic affected student proficiency scores on New York State regents exam tests in 2018 and 2021 in different schools.
Deniz G.
Mentored by Kush Khosla
workspace_premium
A Machine Learning Approach to Understanding the Determining Factors of the Gender Wage Gap

By studying the affect of different attributes on the gender wage gap, we can better understand both the scale of this issue and its possible solutions. So, we explore the question, how does a worker’s marital status, along with other variables, impact the gap in hourly wage between male and female workers? We seek to create a model able to predict the gender wage gap given a set of variables—age, years of education, race, state, and marital status.
Sophia G.
Mentored by Ana Sofia Muñoz Valadez