Collaborative Research: Recruiting STEM Faculty: A systematic analysis of the faculty hiring process at Research Intensive Universities

合作研究：招聘 STEM 教师：对研究型大学教师招聘流程的系统分析

• 批准号: 1535509
• 负责人: Kimberlee Shauman
• 金额: $ 98.73万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535509&HistoricalAwards=false
• 关键词: Collaborative; Research; Recruiting; STEM; Faculty

This proposal was submitted in response to EHR Core Research (ECR) program announcement NSF 15-509. As part of ECR, this project is funded by the Research on Gender in Science and Engineering (GSE) program. GSE seeks to understand and address gender-based differences in science, technology, engineering and mathematics (STEM) education and workforce participation through education and implementation research that will lead to a larger and more diverse domestic STEM workforce. This study will address two core research questions in the area of STEM Workforce Development: Are there gender and racial/ethnic disparities in STEM faculty hiring? If so, what conditions, processes and social contexts generate/mitigate these disparities? The researchers will conduct a systematic theory-driven evaluation of the faculty hiring process by compiling an unprecedented dataset on faculty hiring across ten research-intensive universities. The data will be used to test hypotheses about how gender and race/ethnicity influence applications for faculty positions, evaluation of applicants, and outcomes at multiple stages in the faculty hiring process. By identifying the steps in the hiring process that are most susceptible to bias and the characteristics of the hiring process that amplify/mitigate disparities, this study will identify the most important targets for policy interventions aimed at increasing equity and diversity in faculty hiring.The study will be framed by expectation states theory, which explains how status beliefs (widely shared beliefs that people in one category of a status-based social distinction, such as gender or race, are more socially worthy and competent than those in another category) influence interpersonal interactions, evaluations of self and others, and individual and group behavior to generate and reinforce social inequality. The researchers will construct and use a unique dataset from an online administrative system that compiles information from all faculty recruitments at all University of California campuses. This rich data source includes detailed information on applicant pools, applicant credentials and achievements, hiring processes and committees, and candidates? progression from application through the short list, interview, and offer steps in the process. The richness of the administrative data will be enhanced using automated text analytic tools to generate a dataset that provides large sample sizes, detailed measurement of key variables, links to supplemental data sources that provide measures of influential factors (e.g., institutional prestige), and observable variation in factors which theory predicts affect bias in faculty hiring. Multilevel statistical models will be used to accurately identify and disentangle the influences operating at the faculty search-level versus those operating at the applicant-level to affect the faculty hiring process. Analyses will be disaggregated by STEM discipline, gender, detailed race/ethnicity and race-by-gender categories.

该提案是根据EHR核心研究（ECR）项目公告NSF 15-509提交的。作为ECR的一部分，该项目由科学与工程中的性别研究（GSE）项目资助。GSE旨在通过教育和实施研究，了解和解决科学、技术、工程和数学（STEM）教育和劳动力参与方面的性别差异，这将导致更大、更多样化的国内STEM劳动力。本研究将解决STEM劳动力发展领域的两个核心研究问题：在STEM教师招聘中是否存在性别和种族/民族差异？如果是这样，是什么条件、过程和社会背景产生/减轻了这些差异？研究人员将对教师招聘过程进行系统的理论驱动评估，通过编制10所研究型大学的前所未有的教师招聘数据集。这些数据将用于测试性别和种族/民族如何影响教师职位申请、申请人评估以及教师招聘过程中多个阶段的结果的假设。通过确定招聘过程中最容易受到偏见影响的步骤，以及放大/减轻差异的招聘过程特征，本研究将确定旨在提高教师招聘公平性和多样性的政策干预的最重要目标。该研究将以期望状态理论为框架，该理论解释了地位信念（广泛认同的一种基于地位的社会区别的人，如性别或种族，比其他类别的人更有社会价值和能力）如何影响人际交往，对自我和他人的评估，以及个人和群体行为，从而产生和加强社会不平等。研究人员将从一个在线管理系统中构建并使用一个独特的数据集，该数据集汇集了加州大学所有校区所有教员招聘的信息。这个丰富的数据源包括申请人池、申请人证书和成就、招聘流程和委员会以及候选人的详细信息。从申请到短名单，面试和提供过程中的步骤。管理数据的丰富性将通过使用自动文本分析工具来增强，以生成一个数据集，该数据集提供大样本量、关键变量的详细测量、提供影响因素（例如，机构声望）测量的补充数据源链接，以及理论预测影响教师招聘偏见的因素的可观察变化。多层统计模型将用于准确识别和区分在教师搜索层面和在申请人层面影响教师招聘过程的影响。分析将按STEM学科、性别、详细的种族/民族和种族性别分类进行分类。