How Research, Teaching, and Leadership Roles are Recommended to Male and Female Engineering Faculty Differently

Eugene Judson (), Lydia Ross and Kristi Glassmeyer
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Eugene Judson: Arizona State University
Lydia Ross: Arizona State University
Kristi Glassmeyer: Arizona State University

Research in Higher Education, 2019, vol. 60, issue 7, No 5, 1025-1047

Abstract: Abstract Across disciplines, male faculty spend more time on research than female faculty. Yet, women tend to teach and mentor students more hours than men (Misra et al. in Glob J Eng Educ 14(1):119–131, 2011). These disparities play pivotal roles in tenure and promotion decisions wherein research and leadership roles are most valued (Green in J Soc Work Educ 44(2):117–128, 2008). There is considerable evidence suggesting that implicit biases underpin some of these faculty roles differences, particularly in male-dominated disciplines, such as engineering. At the same time, there is limited evidence that, once aware of gender inequity, individuals will engage in bias correction in order to rectify prejudice. This study was designed to evaluate if implicit bias or bias correction could be detected when faculty considered the most appropriate roles for other faculty. Faculty from 50 colleges of engineering completed an activity wherein they assigned five fictitious engineering faculty characters to five assignments (one research, one leadership, and three teaching/advising roles). One version of the activity contained only male names; the other version was identical except for the change of the middle character’s name from male (Charlie) to female (Cathy). Results indicated that both men and women were significantly more likely to select Cathy for both the leadership and research positions over Charlie. Regression analysis of the Cathy Group data indicated respondents’ gender did not predict selection of Cathy to the leadership role; however, women were significantly more likely than men to select Cathy to do research.

Keywords: Gender; Faculty; Engineering; Bias; Job-sorting; Bias correction; Implicit bias; Gender equity; Higher education (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11162-018-09542-8

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