STEM Workforce Development   STEM Learning and Learning Environments  

Broadening Participation in STEM

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

Effective Years: 2015-2021

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.