Dataset Information

Gender in Science and Engineering Faculties: Demographic Inertia Revisited.

ABSTRACT: The under-representation of women on faculties of science and engineering is ascribed in part to demographic inertia, which is the lag between retirement of current faculty and future hires. The assumption of demographic inertia implies that, given enough time, gender parity will be achieved. We examine that assumption via a semi-Markov model to predict the future faculty, with simulations that predict the convergence demographic state. Our model shows that existing practices that produce gender gaps in recruitment, retention, and career progression preclude eventual gender parity. Further, we examine sensitivity of the convergence state to current gender gaps to show that all sources of disparity across the entire faculty career must be erased to produce parity: we cannot blame demographic inertia.

SUBMITTER: Thomas NR

PROVIDER: S-EPMC4619263 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Gender in Science and Engineering Faculties: Demographic Inertia Revisited.

Thomas Nicole R NR Poole Daniel J DJ Herbers Joan M JM

PloS one 20151021 10

The under-representation of women on faculties of science and engineering is ascribed in part to demographic inertia, which is the lag between retirement of current faculty and future hires. The assumption of demographic inertia implies that, given enough time, gender parity will be achieved. We examine that assumption via a semi-Markov model to predict the future faculty, with simulations that predict the convergence demographic state. Our model shows that existing practices that produce gender ...[more]

PMID: 26488899

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Project description:Based on Social Cognitive Career Theory principles, the present study sought to investigate whether stereotype threat experiences could act as a barrier and reduce the persistence of women in math-intensive activities. More specifically, we assessed whether the experimental activation of stereotypes about women's lower math capabilities affected the performance, persistence, and self-selected difficulty of engineering students in a math task which required sustained effort. We also evaluated the relationships between these effects and the participants' pre-testing gender-science stereotypes and math self-concept. A sample of 340 engineering students (175 and 165 self-identified as males and females, respectively) were recruited and randomly assigned to a control (Con) or stereotype threat (StA) condition to form four similarly sized groups. All participants rated their self-concept in four academic domains (math, chemistry, physics, and coding), completed the gender-science Implicit Association Test, and a "reading comprehension task" that served to promote the stereotype threat manipulation immediately before facing a modified version of the Math Effort Task (M-MET). We observed that, in the control condition, M-MET performance, self-selected difficulty, and persistence were similar in female and male participants, independent of their gender-science implicit stereotypes but correlated to their math self-concept scores. In contrast, the StA condition triggered opposite effects in female and male students, so they showed decreased/enhanced M-MET performance and self-selected difficulty, respectively. This experimental condition also resulted in enhanced persistence of the male students without affecting the number of trials completed by female students. These effects were correlated with the strength of the participants' gender-science implicit stereotypes but not with their math self-concept scores. In fact, as revealed by finer-grain analyses, stereotype threat only had a significant impact on individuals harboring stereotypical gender-science implicit associations. Therefore, it is concluded that: (1) stereotypes can promote differences between male and female engineering students in their performance, self-confidence, and persistence in math-related activities; (2) These effects seem to be more prominent in individuals implicitly perceiving science as a masculine domain. The relevance of these findings to explain women's enhanced abandonment rates of math-intensive studies is discussed.

Project description:Queer identities are often ignored in diversity initiatives, yet there is a growing body of research that describes notable heterosexist and gender-normative expectations in STEM that lead to unsupportive and discriminatory environments and to the lower persistence of queer individuals. Research on the experiences of queer-spectrum individuals is limited by current demographic practices. In surveys that are queer-inclusive there is no consensus on best practices, and individuals with queer genders and queer sexual, romantic, and related orientations are often lumped together in a general category (e.g. LGBTQ+). We developed two queer-inclusive demographics questions and administered them as part of a larger study in undergraduate engineering and computer science classes (n = 3698), to determine which of three survey types for gender (conventional, queered, open-ended) provided the most robust data and compared responses to national data to determine if students with queer genders and/or queer sexual, romantic, and related orientations were underrepresented in engineering and computer science programs. The gender survey with queer-identity options provided the most robust data, as measured by higher response rates and relatively high rates of disclosing queer identities. The conventional survey (male, female, other) had significantly fewer students disclose queer identities, and the open-ended survey had a significantly higher non-response rate. Allowing for multiple responses on the survey was important: 78% of those with queer gender identities and 9% of those with queer sexual, romantic and related orientations selected multiple identities within the same survey question. Queer students in our study were underrepresented relative to national data. Students who disclosed queer gender identities were 7/100ths of the expected number, and those with queer orientations were under-represented by one-quarter. Further work developing a research-based queered demographics instrument is needed for larger-scale changes in demographics practices, which will help others identify and address barriers that queer-spectrum individuals face in STEM.

Dataset Information

Gender in Science and Engineering Faculties: Demographic Inertia Revisited.

Publications

Gender in Science and Engineering Faculties: Demographic Inertia Revisited.

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