Empirical Research: Breaking through the Reputational Ceiling: Professional Networks as a Determinant of Advancement, Mobility, and Career Outcomes for Women and Minorities in STEM

Principal Investigator: 
Project Overview
Background & Purpose: 

The purpose of this study is to address differences in structure, participation, and resources of the professional and collaborative networks of academic STEM scientists with special attention to women and underrepresented minorities. The study asks how and why do the networks of women and URM academic scientists differ from majority networks? How does this differ by institutional type? How do the differences in network characteristics matter for career productivity, satisfaction, and other outcomes?


The focus of this study is on academic scientists in mathematics, biological sciences, and civil engineering in a range of Carnegie classified institutions. While many studies focus on the research intensive institutions, this study takes a broad look at networks across multiple types of higher education institutions, including liberal arts colleges, HBCU's, women's colleges, masters comprehensive, as well as doctoral granting institutions. This structure acknowledges the diversity of institutions in which academic STEM faculty are employed.

Research Design: 

This project has a longitudinal, cross-sectional, and comparative research design and will generate evidence that is both descriptive [interview data] and causal [statistical modeling]. Original data are being collected from STEM faculty in math, civil engineering, and the biological sciences using survey research [self-completion questionnaires and semi-structured interviews]. We will attempt to reach as many under represented faculty as possible within these fields with the goal of a saturated sample. The research includes a series of data collection instruments. The primary tool is an extensive on-line survey. Other instruments include semi-structured interview instruments. The analysis plan includes detailed statistical analysis of traditional survey and social network data.


Findings will be posted as they become available.