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.
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 in this paper 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 showed 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.