A latent class analysis approach to the identification of doctoral students at risk of attrition

Samantha M. Stevens, Peter M. Ruberton, Joshua M. Smyth, Geoffrey L. Cohen, Valerie Purdie Greenaway, Jonathan E. Cook

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1 Scopus citations

Abstract

To advance understanding of doctoral student experiences and the high attrition rates among Science, Technology, Engineering, and Mathematics (STEM) doctoral students, we developed and examined the psychological profiles of different types of doctoral students. We used latent class analysis on self-reported psychological data relevant to psychological threat from 1,081 incoming doctoral students across three universities and found that the best-fitting model delineated four threat classes: Lowest Threat, Nonchalant, Engaged/Worried, and Highest Threat. These classes were associated with characteristics measured at the beginning of students' first semester of graduate school that may influence attrition risk, including differences in academic preparation (e.g., amount of research experience), selfevaluations and perceived fit (e.g., sense of belonging), attitudes towards graduate school and academia (e.g., strength of motivation), and interpersonal relations (e.g., perceived social support). Lowest Threat students tended to report the most positive characteristics and Highest Threat students the most negative characteristics, whereas the results for Nonchalant and Engaged/Worried students were more mixed. Ultimately, we suggest that Engaged/Worried and Highest Threat students are at relatively high risk of attrition. Moreover, the demographic distributions of profiles differed, with members of groups more likely to face social identity threat (e.g., women) being overrepresented in a higher threat profile (i.e., Engaged/Worried students) and underrepresented in lower threat profiles (i.e., Lowest Threat and Nonchalant students). We conclude that doctoral students meaningfully vary in their psychological threat at the beginning of graduate study and suggest that these differences may portend divergent outcomes.

Original languageEnglish (US)
Article numbere0280325
JournalPloS one
Volume18
Issue number1 January
DOIs
StatePublished - Jan 2023

All Science Journal Classification (ASJC) codes

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