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：研究密集型大学教师招聘流程的系统分析

• 批准号: 1535435
• 负责人: Catherine Albiston
• 金额: $ 49.18万
• 依托单位: University of California-Berkeley
• 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=1535435&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.

该提案是为了响应EHR核心研究（ECR）计划公告NSF 15-509而提交的。作为ECR的一部分，该项目由科学和工程性别研究（GSE）计划资助。GSE旨在通过教育和实施研究来了解和解决科学，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。这项研究将解决两个核心的研究问题，在干劳动力发展领域：是否有性别和种族/民族的差距在干教师招聘？如果是，是什么条件、进程和社会背景造成/减轻了这些差距？研究人员将对教师招聘过程进行系统的理论驱动评估，方法是编制一个前所未有的关于10所研究密集型大学教师招聘的数据集。这些数据将用于测试有关性别和种族/民族如何影响教师职位的申请，申请人的评估以及教师招聘过程中多个阶段的结果的假设。通过识别招聘过程中最容易受到偏见影响的步骤以及招聘过程中放大/减轻差异的特征，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。这就解释了地位信念（人们普遍认为，处于基于身份的社会区别类别中的人，如性别或种族，比其他类别的人更有社会价值和能力）影响人际交往，自我和他人的评价，以及个人和群体行为，以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和使用一个独特的数据集，该系统收集了加州大学所有校区的所有教师招聘信息。这一丰富的数据源包括有关申请人人才库、申请人资质和成就、招聘流程和委员会以及候选人从申请到入围、面试和录取的详细信息。将使用自动文本分析工具来增强行政数据的丰富性，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、与提供影响因素测量的补充数据源的链接（例如，机构声望），以及理论预测的影响教师招聘偏见的因素的可观察变化。多层次的统计模型将被用来准确地识别和理清在教师搜索层面上操作的影响与那些在申请人层面上操作，以影响教师招聘过程。分析将按STEM学科、性别、详细的种族/民族和按性别分类的种族进行分类。