It is in universities’ interest to have high numbers of thriving and successful PhD candidates. PhD students are an essential part of the research system, as Larivière (2012) showed that one-third of the research output at universities was produced by PhD students. Moreover, the unsuccessful completion of a PhD trajectory goes hand in hand with major financial, societal and psychological costs (Allan and Dory 2001; Golde 2005).

Aggregated across Europe, about 34% of all PhD students do not obtain their PhD degree within six years (Hasgall, Saenen, and Borrell-Damian 2019). In Australian, British, Canadian, and American universities, average dropout rates range between 30% and 50%, depending on the discipline (Bowen and Rudenstine 2014; Lovitts 2002; Golde 2005; Council of Graduate Schools 2008).

the PhD track is very different from other phases of education and brings along its specific challenges. A substantial group of PhD students works alone on their project under the supervision of one or more supervisors. Working collaboratively with peers is not always part of the PhD trajectory, which can sometimes make it a lonely process and renders the role of the supervisor all the more important (Cantor 2020). Additionally, the academic environment in which PhD students work is characterized by ever-increasing job demands and competition, due to among other things a growing number of undergraduate students who increasingly fall under the responsibility of PhD students, an increasing pressure to get research funding and publish, and a growing demand to be involved in other activities next to research (Gill 2014). Both the high dependency on the supervisor and the demanding academic environment might incentivise PhD students to quit.

Indeed, research found that factors related to supervision, the project itself and psychosocial factors are associated with the intention to quit the PhD (van Rooij, Fokkens-Bruinsma, and Jansen 2021). However, turnover intention does not always reliably predict actual turnover, nor are the variables explaining turnover intention necessarily the same as those explaining actual turnover (Cohen, Blake, and Goodman 2016). Therefore, we add to this line of work by studying how the received support of the supervisor, the experienced time pressure during the project, and the amount of passion one has for research can predict actual dropout. Contrary to previous studies – that tend to focus solely on administrative data or survey data – we combine administrative data on actual dropout with survey data on the experiences of the doctoral trajectory (n=589). 

In this study, special attention is paid to the heterogeneity within the group of PhD students. Previous research does suggest that dropout rates between disciplines differ (Golde 1994; Wright and Cochrane 2000), yet deeper knowledge on the mechanisms behind this is lacking. The aim of this study, then, is to investigate whether certain characteristics of PhD students and certain experiences of the PhD trajectory are associated with dropout, and how the importance of these variables varies between scientific disciplines. These insights will enable university policymakers to develop targeted measures to reduce dropout. Specifically, the two research questions for this article are: “to what extent do support, time pressure and passion for research predict dropout?” and “does their potential predictive power vary across scientific disciplines?”. 

To answer our research questions, we rely on longitudinal data from the VUB PhD Survey as well as administrative data of PhD students of the Vrije Universiteit Brussel (VUB). The VUB PhD Survey is organized on an annual basis and contains information on the subjective experiences of PhD students. The response rates for the waves vary between 42% and 49%. The data of the VUB PhD survey were matched with administrative information on the current administrative enrolment status of PhD students: (1) successfully completed the PhD programme, (2) still active in the programme, or (3) dropped out of the programme. For this paper, the used data was limited to the VUB PhD Survey waves from 2018 to 2021 and restrict the sample to PhD students who were in their first year of enrolment when completing the survey (n=589). The combination of administrative data on the enrolment status with survey data on the subjective experiences of PhD students during their first year of enrolment enable us to investigate the effects of subjective indicators at moment t on moment t+1, and see whether they can predict dropout. Moreover, the university-wide data enable us to study differences within the heterogenous group of PhD students, by focusing on a group of PhD students (1) from various disciplines who (2) work under different contracts. The dependent variable is a dummy-coded variable that indicates whether a PhD student dropped out. The independent variables are “experienced time pressure”, “satisfaction with supervisor support”, “passion for research”. Control variables were gender, nationality (Belgian or foreign), doctoral school (as a proxy for discipline) and the type of contract (teaching assistant, project funding, personal mandate, self-financed or other). We used a two-step analysis to answer our research questions. Firstly, we performed a logistic regression analysis predicting dropout. Model 1 included background characteristics only (gender, nationality, doctoral school, and type of contract). In separate models, we successively combined the background characteristics with the following predictor variables: the experienced support of the supervisor during the first year (model 2), the experienced time pressure during the first year (model 3), and passion of PhD students for their research in the first year (model 4). The fifth and final model included all variables. Secondly, we stratified the final model by doctoral schools. We tested whether the effect parameters varied significantly between disciplines using calculations suggested by Paternoster and colleagues (1998).

Results show that supervisor support is negatively related to dropout, and that this is especially important for PhD students in the human sciences. Time pressure is positively related to dropout. When stratified by scientific discipline, this effect was only significant for PhD students in human sciences and in the life sciences and medicine. Passion for research showed a negative association with dropout. Stratification by discipline showed that this effect was only found among PhD students in natural sciences and engineering. Furthermore, teaching assistants showed higher dropout rates, and female PhD students in human sciences and life sciences and medicine were less likely to drop out. The findings highlight the need for universities to be aware of the diversity of PhD students when formulating support policies for PhD students. These policies could include facilitating supervisors to support academic integration of first-year PhD students and create better job resources; monitoring the implementation of research plans and the balance between research and teaching or clinical tasks to reduce experience time pressure; or facilitating state-of-the art research infrastructure to keep PhD students passionate about their research. Finally, special attention should be paid to the needs of teaching assistants, specifically to those in the human sciences, because even after taking supervisor support, time pressure and passion for research into account, they are still more likely to drop out.

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