Identifying and Reducing Gender Bias in STEM: Systematically Synthesizing the Experimental Evidence

该项目将整合高质量的实验证据，以减少科学，技术，工程和数学（STEM）领域的性别偏见，包括性别偏见如何与种族和民族等其他身份交叉。偏袒男性的偏见可能会在许多方面阻碍女性在STEM领域的培训和职业生涯，但研究也表明，有希望的干预措施可以改变有偏见的文化和结构。该项目将综合四十年的研究，以了解中学后和劳动力环境，其中对STEM中女性的偏见仍然特别有害，以及可以最有效地减少这种偏见的干预措施。这些结果将为学术辩论提供信息，例如对STEM中女性的偏见是否随着时间的推移而减少，是否在几乎所有的培训和职业环境中持续存在，或者在不同的环境和STEM领域中以更细微的方式变化。这项工作的最终目的是帮助各组织（a）打破可能直接阻碍妇女进入STEM领域的同侪和导师歧视文化，（B）减轻可能使妇女退出STEM领域的歧视和排斥的累积经验。项目研究结果的传播将特别关注高等教育机构的可操作见解，例如男女STEM教师在教学，指导和服务活动中可以采用的减少偏见策略。该项目由EHR核心研究（ECR）计划资助，该计划支持推进STEM学习和学习环境的基础研究，扩大STEM参与和STEM劳动力发展的工作。综合将包括两组研究：（a）测试STEM领域是否存在性别偏见的研究（偏差识别研究）和（B）评估减少此类偏差的干预措施的研究（偏差减少研究）。对于这两种情况，综合将侧重于随机实验设计，例如将简历上的名字从John改为Jennifer（偏见识别研究）或分配一些人接受或不接受多样性培训（偏见减少研究）。专注于实验设计最大限度地提高了证据的严谨性，因为它们有助于在测试偏倚和评估干预效果时排除潜在的混淆。所有STEM领域都将有资格接受审查，涵盖本科教育到学术和非学术劳动力，以提供对性别偏见存在的地方以及干预措施如何减少这些偏见的强大而全面的了解。与传统的文献综述相比，该团队将使用严格的系统综述和荟萃分析方法，以提高审查过程的透明度，减少审查者的偏见，并确保项目的结果是稳健的，全面的现有高质量证据。统计分析将侧重于理解特定的上下文特征（例如，正式问责制、学科领域、干预设计）可以解释在确定和减少STEM中的性别偏见方面的混合结果。这些知识可以帮助大学、公司和其他组织确定哪些地方最需要有针对性的干预，以及哪些策略对减轻偏见最有效。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will integrate high-quality experimental evidence on the existence of, and strategies to reduce, gender bias in science, technology, engineering, and mathematics (STEM) fields, including how gender bias may intersect with other identities such as race and ethnicity. Biases favoring men could thwart women’s training and careers in STEM fields in many ways, but research also suggests promising interventions for changing biased cultures and structures. This project will synthesize four decades of research to understand the postsecondary and workforce contexts in which bias against women in STEM remains especially pernicious and the interventions that can most effectively reduce such biases. The results will inform scholarly debates such as whether biases against women in STEM have reduced over time, persist in nearly all training and career contexts, or vary in more nuanced ways across contexts and STEM fields. The work ultimately aims to help organizations (a) disrupt the culture of peer and mentor discrimination that could directly block women’s entry into STEM fields and (b) mitigate the accumulated experiences of discrimination and exclusion that could drive women out of STEM. Dissemination of project findings will especially focus on actionable insights for higher education institutions, such as bias reduction strategies that male and female STEM faculty can adopt in their teaching, mentoring, and service activities. This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.The synthesis will include two sets of studies: (a) studies testing for the existence of gender bias in STEM fields (bias identification studies) and (b) studies evaluating interventions to reduce such biases (bias reduction studies). For both, the synthesis will focus on randomized experimental designs, such as changing the name on a résumé from John to Jennifer (bias identification study) or assigning some individuals to receive diversity training or not (bias reduction study). Focusing on experimental designs maximizes the rigor of the evidence to be synthesized because they help rule out potential confounds when testing for bias and evaluating intervention efficacy. All STEM fields will be eligible for review, spanning undergraduate education to the academic and nonacademic workforce, to provide a robust and thorough understanding of where gender biases exist and how interventions can reduce them. Contrasting with traditional literature reviews, the team will use rigorous systematic review and meta-analysis methods to improve transparency of the review process, reduce reviewer bias, and ensure the project’s findings are robust and comprehensive of existing high-quality evidence. Statistical analyses will focus on understanding how specific contextual features (e.g., formal accountability, disciplinary field, intervention design) can explain mixed findings on identifying and reducing gender bias in STEM. This knowledge can help universities, companies, and other organizations pinpoint where targeted intervention is most needed and which strategies will be most effective for mitigating bias.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.