Climate Control: Gender and Racial Bias in Engineering?

Su Li; Roberta Rincon; joan Chalmers Williams

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Conference: 2017 ASEE Annual Conference & Exposition
Location: Columbus, Ohio
Publication Date: June 24, 2017
Start Date: June 24, 2017
End Date: June 28, 2017
Conference Session: Action on Diversity - Engineering Workforce & Faculty Training
Tagged Topics: Diversity and ASEE Diversity Committee
Page Count: 43
DOI: 10.18260/1-2--28038
Permanent URL: https://peer.asee.org/28038
Download Count: 936

Paper Authors

biography

Su Li U. C. Hastings, College of the Law

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Su Li is the Director of Research on Organization Bias at the Center for WorkLife Law. Su is a quantitative sociologist with a background in using quantitative research methods in empirical legal studies research. Her research interests include quantitative methods, the legal profession, law and society, and gender and social inequality. She has published collaboratively in law review journals (such as Stanford Law Review, California Law Review, and Arizona Law Review) and peer review journals (such as Law and Social Inquiry, Sociology of Education, and Gender and Society) on topics including the legal profession, intellectual property, and constitutional law.

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Roberta Rincon Society of Women Engineers

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Dr. Rincon joined the Society of Women Engineers in February 2016 as the Manager of Research, where she oversees the organization's research activities around female engineers from elementary through college and into the workforce. With over 15 years of experience in higher education administration, including as a Senior Research and Policy Analyst for The University of Texas System, her focus has been on understanding the factors that impact student success and influencing the policies that support students from high school through college completion. Her responsibilities have included managing various award and faculty recruitment programs, analyzing the impact of state legislative actions, coordinating efforts to increase resilience among college students, and preparing white papers on topics ranging from classroom utilization to student success.
Dr. Rincon received her B.S. in Civil Engineering from The University of Texas at Austin, an MBA and an M.S. in Information Management from Arizona State University, and a Ph.D. in Educational Policy and Planning from The University of Texas at Austin.

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joan Chalmers Williams University of California, Hastings College of the Law

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Joan C. Williams is Distinguished Professor of Law, Hastings Foundation Chair, and Founding Director of the Center for WorkLife Law at the University of California, Hastings College of the Law. She has written extensively on gender bias and women in STEM, with work published in sociology, psychology and law journals.

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Abstract

The concept of implicit bias is typically studied by behavioral and cognitive psychologists who seek to gain information on brain patterns versus how those patterns show up in the workplace. Thirty years of social science research have documented that although explicit bias against women and other under-represented groups is far less common today, subtle (or implicit) bias remains rampant. There have not been many studies systematically measuring implicit bias in daily interactions (categorized into four types of bias: Prove-It-Again, Tightrope, Tug of War, and Maternal Wall) and at different stages for workplace process (eg. Hiring, performance evaluations, etc.) In this research, we reached out to thousands of engineers in the U.S. with a Workplace Experiences Survey focusing on implicit bias. The survey includes 38 Likert scale questions asking respondents to rate their agreement level of statements describing experience with implicit bias in the workplace. Over 3000 respondents with at least of two years of work experience completed the survey. Nearly one-third of them left comments describing related experience at their workplace. We also interviewed a number of senior female engineers who shared their experiences with implicit bias during their career. We conducted statistical analysis (ANOVA, regression analysis) and text analysis of the quantitative and qualitative data. Findings from both data sources show that women and people of color experienced more implicit bias at work than white men. Regression analyses showed that, after controlling for age, education, workplace seniority, and academic status, women still reported more Prove-It-Again, Tightrope, and Maternal Wall bias, and Asian and African-American engineers reported more Prove-It-Again and Tightrope bias, than their white male counterparts. Regression analysis showed that, after controlling for the above-mentioned variables, women reported experiencing higher levels of bias in hiring, networking/sponsorship, and promotion than their male counterparts. Regression analysis showed that, after controlling for above-mentioned variables, African-American engineers reported higher levels of bias in networking, promotion, and mentoring/sponsorship than their white counterparts. Asian-American engineers reported more bias in performance evaluations than their white counterparts.

Citation
Format

Li, S., & Rincon, R., & Williams, J. C. (2017, June), Climate Control: Gender and Racial Bias in Engineering? Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28038

TY - CPAPER
AB - The concept of implicit bias is typically studied by behavioral and cognitive psychologists who seek to gain information on brain patterns versus how those patterns show up in the workplace. Thirty years of social science research have documented that although explicit bias against women and other under-represented groups is far less common today, subtle (or implicit) bias remains rampant. There have not been many studies systematically measuring implicit bias in daily interactions (categorized into four types of bias: Prove-It-Again, Tightrope, Tug of War, and Maternal Wall) and at different stages for workplace process (eg. Hiring, performance evaluations, etc.)
In this research, we reached out to thousands of engineers in the U.S. with a Workplace Experiences Survey focusing on implicit bias. The survey includes 38 Likert scale questions asking respondents to rate their agreement level of statements describing experience with implicit bias in the workplace. Over 3000 respondents with at least of two years of work experience completed the survey. Nearly one-third of them left comments describing related experience at their workplace. We also interviewed a number of senior female engineers who shared their experiences with implicit bias during their career. We conducted statistical analysis (ANOVA, regression analysis) and text analysis of the quantitative and qualitative data. Findings from both data sources show that women and people of color experienced more implicit bias at work than white men.
Regression analyses showed that, after controlling for age, education, workplace seniority, and academic status, women still reported more Prove-It-Again, Tightrope, and Maternal Wall bias, and Asian and African-American engineers reported more Prove-It-Again and Tightrope bias, than their white male counterparts. Regression analysis showed that, after controlling for the above-mentioned variables, women reported experiencing higher levels of bias in hiring, networking/sponsorship, and promotion than their male counterparts. Regression analysis showed that, after controlling for above-mentioned variables, African-American engineers reported higher levels of bias in networking, promotion, and mentoring/sponsorship than their white counterparts. Asian-American engineers reported more bias in performance evaluations than their white counterparts.

AU - Su Li
AU - Roberta Rincon
AU - joan Chalmers Williams
CY - Columbus, Ohio
DA - 2017/06/24
PB - ASEE Conferences
TI - Climate Control: Gender and Racial Bias in Engineering?
UR - https://peer.asee.org/28038
DO - 10.18260/1-2--28038
ER -