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  1. Abstract Background

    Postdoctoral training holds an increasingly important place in preparation for leading academic and research positions. While little empirical research has described postdoctoral training beyond the sciences, across all fields, “misaligned expectations” are often touted as a key source of postdoctoral strife.

    Purpose/Hypothesis

    This article describes mentorship competency beliefs within engineering and computer science fields, which increasingly engage in postdoctoral training.

    Design/Method

    An embedded mixed‐methods design was used to quantitatively identify mentorship profiles from survey data using latent profile analysis (LPA) from a sample ofn = 118 postdoctoral scholars andn = 165 postdoctoral supervisors. Qualitative thematic analysis of interviews withn = 29 postdoctoral scholars andn = 20 postdoctoral supervisors was used to identify meaning in the differences between quantitative profiles. The combination of LPA with thematic analysis enabled the triangulation of distinct postdoctoral mentorship profile definitions.

    Results

    LPA identified six postdoctoral fellow profiles and four supervisor profiles, which became clearly definable through thematic analysis. Postdoc profiles included Technical Manager, Autonomy Focused Advisor, Stretched Mentor, Well‐Rounded Mentor, Exemplar Mentor, and Leader‐Mentor, while supervisor profiles included Autonomous Mentor, Reflective Mentor, Research Lab Mentor, and Confident Leader‐Mentor. Some of these are aligned, but several are not, giving insight into the phenomenon of “misaligned expectations” in postdoctoral literature.

    Conclusions

    The mentorship profiles illustrate the misalignment in expectations, which leads to negative mentorship experiences for many postdoctoral scholars.

     
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    Free, publicly-accessible full text available July 16, 2025
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  4. The purpose of this research full paper is to investigate issues facing very early-stage master’s students as they transition into a degree program at a large research-intensive university. While there is an increasing focus on graduate and doctoral engineering education, few studies have sought to focus specifically on master’s students, treating them from a research perspective as miniature doctoral students, though it is documented that MS students in engineering have different goals and motivations for pursuing graduate study than PhD students, as well as different anticipated career trajectories. To further compound these gaps in the literature, most studies assume that doctoral students in engineering come from historically privileged socioeconomic backgrounds. National conversations are clear that to broaden participation in engineering, the educational community must attend to the specific needs of students from low-income backgrounds. These students may also not have access to the social and cultural capital required to navigate graduate school, since many are first-generation graduate students and because systems of education are traditionally designed for students from upper class backgrounds. To this end, this study explores the experiences of first-semester graduate students supported in part by funding aimed to support master’s students and have demonstrated unmet financial need. Interviews were conducted with six first- and second year master’s students and analyzed using thematic analysis methods employing Posselt’s Framework for Doctoral Student Support—here, extended to master’s students—to elicit information about surprises, expectations, and unanticipated issues facing this special population of students. Findings indicate that there are several easily implemented structural modifications programs and faculty can take that can facilitate the transition to graduate school for graduate students, low-income and otherwise. 
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    Free, publicly-accessible full text available June 24, 2025
  5. Free, publicly-accessible full text available December 1, 2024
  6. Introduction

    Despite efforts to increase the participation of marginalized students in Science, Technology, Engineering, and Mathematics (STEM), neurodivergent students have remained underrepresented and underserved in STEM graduate programs. This qualitative study aims to increase understanding of the experiences of neurodivergent graduate students pursuing advanced degrees in STEM. In this analysis, we consider how common graduate school experiences interface with the invisibility of neurological diversity, thus contributing to a set of unique challenges experienced by neurodivergent students.

    Materials and methods

    In this qualitative study, 10 focus group sessions were conducted to examine the experiences of 18 students who identify as neurodivergent in graduate STEM programs at a large, research-intensive (R1) university. We used thematic analysis of the transcripts from these focus groups to identify three overarching themes within the data.

    Results

    The findings are presented through a novel model for understanding neurodivergent graduate STEM student experiences. The findings suggest that students who identify as neurodivergent feel pressure to conform to perceived neurotypical norms to avoid negative perceptions. They also may self-silence to maintain stability within the advisor-advisee relationship. The stigma associated with disability labels contributes a heavy cognitive and emotional load as students work to mask neurodiversity-related traits, navigate decisions about disclosure of their neurodivergence, and ultimately, experience significant mental health challenges and burnout. Despite these many challenges, the neurodivergent graduate students in this study perceived aspects of their neurodivergence as a strength.

    Discussion

    The findings may have implications for current and future graduate students, for graduate advisors who may or may not be aware of their students’ neurodivergence, and for program administrators who influence policies that impact the wellbeing and productivity of neurodivergent students.

     
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  7. Aim/Purpose: The research reported here aims to demonstrate a method by which novel applications of qualitative data in quantitative research can resolve ceiling effect tensions for educational and psychological research.Background: Self-report surveys and scales are essential to graduate education and social science research. Ceiling effects reflect the clustering of responses at the highest response categories resulting in non-linearity, a lack of variability which inhibits and distorts statistical analyses. Ceiling effects in stress reported by students can negatively impact the accuracy and utility of the resulting data.Methodology: A longitudinal sample example from graduate engineering students’ stress, open-ended critical events, and their early departure from doctoral study considerations demonstrate the utility and improved accuracy of adjusted stress measures to include open-ended critical event responses. Descriptive statistics are used to describe the ceiling effects in stress data and adjusted stress data. The longitudinal stress ratings were used to predict departure considerations in multilevel modeling ANCOVA analyses and demonstrate improved model predictiveness.Contribution: Combining qualitative data from open-ended responses with quantitative survey responses provides an opportunity to reduce ceiling effects and improve model performance in predicting graduate student persistence. Here, we present a method for adjusting stress scale responses by incorporating coded critical events based on the Taxonomy of Life Events, the application of this method in the analysis of stress responses in a longitudinal data set, and potential applications.Findings: The resulting process more effectively represents the doctoral student experience within statistical analyses. Stress and major life events significantly impact engineering doctoral students’ departure considerations.Recommendations for Practitioners: Graduate educators should be aware of students’ life events and assist students in managing graduate school expectations while maintaining progress toward their degree. Recommendation for Researchers: Integrating coded open-ended qualitative data into statistical models can increase the accuracy and representation of the lived student experience. The new approach improves the accuracy and presentation of students’ lived experiences by incorporating qualitative data into longitudinal analyses. The improvement assists researchers in correcting data with ceiling effects for use in longitudinal analyses.Impact on Society: The method described here provides a framework to systematically include open-ended qualitative data in which ceiling effects are present.Future Research: Future research should validate the coding process in similar samples and in samples of doctoral students in different fields and master’s students. 
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