**Manuscript Title:** A Latent Class Analysis Approach to the Identification of Doctoral Students at Risk of Attrition **Abstract:** To advance understanding of doctoral student experiences and the high attrition rates among STEM doctoral students, we developed and examined the psychological profiles of different types of doctoral students. We used latent class analysis (LCA) 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 predicted 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), self-evaluations (e.g., self-efficacy), attitudes towards graduate school and academia (e.g., strength of motivation), and interpersonal relations and perceived fit (e.g., sense of belonging). *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. *Keywords:* latent class analysis, doctoral education, social identity threat, attrition