Dissertations, Theses, and Capstone Projects
Date of Degree
6-2021
Document Type
Capstone Project
Degree Name
M.S.
Program
Data Analysis & Visualization
Advisor
Aucher Serr
Subject Categories
Applied Statistics | Categorical Data Analysis | Digital Humanities | Feminist, Gender, and Sexuality Studies | Women's Studies
Keywords
gender, pay gap, statistics, website, data, technology
Abstract
In the United States, a significant population is facing an uphill battle trying to thrive in an industry that has seen exponential growth in recent years. Women, who account for approximately 50.8% of the U.S. population are statistically underpaid and underrepresented in science, technology, engineering, and mathematics (STEM). Despite women-led technology teams establishing a 21% greater return on investment than teams who don’t, and young women largely outperforming men in math according to a 2015 study, there are only three fortune 500 companies led by women, and they comprise only 10% of internet entrepreneurs. Research generates hundreds of articles, infographics, and reports showing that women are capable of, yet not thriving in technology occupations. Brent Ozar Unlimited and PayScale have provided data from the past seven years on top tech companies demographics and salaries. I have analyzed this data using various statistical methods and created Envision Equality. Using this platform, I’ve visualized and displayed data from these sources in the form of charts and infographics. The intention of this project is to start a dialogue surrounding the issue of gender discrimination in technology occupations, and begin to address it.
Recommended Citation
Bolewicki, Quinn, "Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology" (2021). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/4418
Capstone Project Website. Archived website as a WARC file, created using webrecorder.io – web archive player available at https://github.com/webrecorder/webrecorderplayer-electron
Capstone_site-master.zip (4330 kB)
Capstone Project Code Repository
Included in
Applied Statistics Commons, Categorical Data Analysis Commons, Digital Humanities Commons, Women's Studies Commons
Comments
Online component: https://qbolewicki.github.io/Capstone_site/