The importance of collaboration and supervisor behaviour for gender differences in doctoral student performance and early career development

ABSTRACT

This article provides an explanation for previously observed gender differences in scientific performance during doctoral studies and the early career. Data is based on doctoral students in science, technology, and medicine at a Swedish university. We collected information on each doctoral student's publication and employment history. We also created publication histories for the doctoral candidates main supervisors. The data was supplemented with information on gender, age, and research area. Informed by theories on academic socialization, our research questions focus on how gender differences in productivity during doctoral studies and the early career relate to research collaboration and behaviour/characteristics of the main supervisor. Results show that the gender gap in productivity during doctoral studies, and the early career, can be explained by the degree to which the doctoral students co-author publications with their main supervisors and the size of their collaborative networks.

KEYWORDS:

• Gender gap
• doctoral student
• early career
• research performance
• productivity puzzle
• Academic socialization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Our research questions are presented at the end of the ‘Previous research and theoretical framework’ section.

2 Transforming the publication variable into a geometric standard score, we assume a log normal distribution of errors, which has been confirmed in the diagnostic procedure. However, the interpretation of geometric standard scores is not easy and suggests the need for further elaboration. First, since there are zeros in the predictors, we added one to each observation. The geometric standard score, z_i, is calculated as $z_i=\frac{\mathrm{l}\mathrm{n}\left(x_i\right)-\mathrm{l}\mathrm{n}\left({\mu }_g\right)}{\mathrm{l}\mathrm{n}\left({\sigma }_g\right)}=lo{g}_{{\sigma }_g}\left(\frac{x_i}{{\mu }_g}\right)$where x_i is the observed number of publications, ${\mu }_g$ is the geometric mean for the research area, and ${\sigma }_g$ is the geometric standard deviation for the research area. The distribution of all z_i is approximately normal with mean 0 and standard deviation 1. If the ratio x_i/${\mu }_g$ = ${\sigma }_g$, then z_i = 1, and if the ratio x_i /${\mu }_g$ = 1/${\sigma }_g$, then z_i = -1, and so on. The main advantage of using this transformation in our case is that we normalize the variation between research areas, and publication volume is less sensitive for extreme values.

3 We used leave-one-out cross-validation to test the model fit of the different modelling techniques. Tested alternatives were Poisson regression, over-dispersed Poisson regression, negative binominal regression, and OLS regression.

Additional information

Funding

This work was supported by Vetenskapsrådet [grant number 2016-05205].