The purpose of this paper is to describe how one group of ADVANCE Project researchers investigated faculty co-authorship networks to identify relationships between women’s positions in these networks, their research productivity and their advancement at the university – and to make those relationships transparent.
Multiple methods for capturing faculty network data were evaluated, including collecting self-reported data and mining bibliometric data from various web-based sources. Faculty co-authorship networks were subsequently analyzed using several methodologies including social network analysis (SNA), network visualizations and the Kaplan–Meier product limit estimator.
Results suggest that co-authorship provides an important way for faculty to signal the value of their work, meaning that co-authoring with many others may be beneficial to productivity and promotion. However, patterns of homophily indicate that male faculty tend to collaborate more with other men, reducing signaling opportunities for women. Visualizing these networks can assist faculty in finding and connecting with new collaborators and can provide administrators with unique views of the interactions within their organizations. Finally, Kaplan–Meier survival studies showed longitudinal differences in the retention and advancement of faculty based on gender.
Together, these findings begin to shed light on subtle differences that, over time, may account for the significant gender disparities at STEM institutions, patterns which should be investigated and addressed by administrators. Lessons learned, as well as the novel use of SNA and Kaplan–Meier in investigating gender differences in STEM faculty, provide important findings for other researchers seeking to conduct similar studies at their own institutions.
The authors gratefully acknowledge the work of many individuals who assisted in this research, particularly Dr Yi-Fang (Brook) Wu, Dr Mingzhu Zhu, Dr Katia Passerini and Dr Christopher Markson.
Collins, R. and Steffen-Fluhr, N. (2019), "Hidden patterns: Using social network analysis to track career trajectories of women STEM faculty", Equality, Diversity and Inclusion, Vol. 38 No. 2, pp. 265-282. https://doi.org/10.1108/EDI-09-2017-0183
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