Mapping scientists’ career trajectories in the survey of doctorate recipients using three statistical methods
Mapping scientists’ career trajectories in the survey of doctorate recipients using three statistical methods
Authors: Kathryn Anne Edwards, Hannah Acheson-Field, Stephanie Rennane, Melanie A. Zaber
Citation: Edwards, Kathryn Anne and Acheson-Field, Hannah and Rennane, Stephanie and Zaber, Melanie A. (2023). Mapping scientists’ career trajectories in the survey of doctorate recipients using three statistical methods. Scientific Reports, 13(1), 8119.
Abstract: This paper investigates to what extent there is a ‘traditional’ career among individuals with a Ph.D. in a science, technology, engineering, or math (STEM) discipline. We use longitudinal data that follows the first 7–9 years of post-conferral employment among scientists who attained their degree in the U.S. between 2000 and 2008. We use three methods to identify a traditional career. The first two emphasize those most commonly observed, with two notions of commonality; the third compares the observed careers with archetypes defined by the academic pipeline. Our analysis includes the use of machine-learning methods to find patterns in careers; this paper is the first to use such methods in this setting. We find that if there is a modal, or traditional, science career, it is in non-academic employment. However, given the diversity of pathways observed, we offer the observation that traditional is a poor descriptor of science careers.
Reading Notes
Objective
To understand the career trajectories of individuals with a Ph.D.
Background
Careers in science are often described as a pipeline, with attrition at each stage and each stage necessary for the next, with the ideal job being the end-point of that pipeline - a tenured research professor
Data & Key Variables
Survey of Earned Doctorates - Initial placement: academic, non-academic, post-doc, not working/unknown
Survey of Doctoral Recipients - 3-5 years and 7-9 years post-PhD: tenure-track academic, non-tenure-track academic, non-academic, post-doc, not working
Methodology
Use 3 ways of categorizing career trajectories:
Bottom-up classification of most common trajectories
Machine learning - algorithmic sequence analysis (TraMineR) to group similar trajectories
Top down classification based on pipeline concept
Results
The most common career path post-Ph.D. was the non-academic path
From pipeline-based classification: “Pipers” stay on academic pipeline - 21% of all PhDs in sample, 37% of social science
“Nevers” leave the academic pipeline at initial placement and don’t return - 39% All, 30% social science
“Droppers” start on academic pipeline & drop off - 29% All, 20% social science
“Hoppers” move back to academia after being out - 11% all, 13% social science
Key Table/Figure
