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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. 

Abstract BackgroundWhile studies examining graduate engineering student attrition have grown more prevalent, there is an incomplete understanding of the plight faced by persisting students. As mental health and well‐being crises emerge in graduate student populations, it is important to understand how students conceptualize their well‐being in relation to their decisions to persist or depart from their program. Purpose/HypothesisThe purpose of this article is to characterize the well‐being of students who endured overwhelming difficulties in their doctoral engineering programs. The PERMA‐V framework of well‐being theory proposes that well‐being is a multifaceted construct comprised ofpositive emotion,engagement,relationships,meaning,accomplishment, andvitality. Design/MethodData were collected in a mixed‐methods research design through two rounds of qualitative semistructured interviews and a survey‐based PERMA‐V profiling instrument. Interview data were analyzed thematically using the PERMA‐V framework as an a priori coding schema and narrative configuration and analysis. ResultsThe narratives demonstrated the interconnectedness between the different facets of well‐being and how they were influenced by various experiences the participants encountered. The participants in this study faced prolonged and extreme adversity. By understanding how the multiple dimensions of well‐being theory manifested in their narratives, we better understood and interpreted how these participants chose to persist. 

Abstract BackgroundWhile previous work in higher education documents the impact of high tuition costs of attending graduate school as a key motivator in attrition decisions, in engineering, most graduate students are fully funded on research fellowships, indicating there are different issues causing individuals to consider departure. There has been little work characterizing nonfinancial costs for students in engineering graduate programs and the impact these costs may have on persistence or attrition. Purpose/HypothesisFramed through the lens of cost as a component of the expectancy–value theory framework and the graduate attrition decisions (GrAD) model conceptual framework specific to engineering attrition, the purpose of this article is to characterize the costs engineering graduate students associate with attending graduate school and document how costs affect students' decisions to persist or depart. Design/MethodData were collected through semistructured interviews with 42 engineering graduate students from R1 engineering doctoral programs across the United States who have considered, are currently considering, or have chosen to depart from their engineering PhD programs with a master's degree. ResultsIn addition to time and money, which are costs previously captured in research, participants identified costs to life balance, costs to well‐being, and identify‐informed opportunity costs framed in terms of what “could have been” if they had chosen to not go to graduate school. As these costs relate to persistence, students primarily identified their expended effort and already‐incurred costs as the primary motivator for persistence, rather than any expected benefits of a graduate degree. ConclusionThe findings of this work expand the cost component of the GrAD model conceptual framework, providing a deeper understanding of the costs that graduate students relate to their persistence in engineering graduate programs. It evidences that motivation to persist may not be due to particularly strong goals but may result from costs already incurred. Through this research, the scholarly community, students, advisors, and university policymakers can better understand the needs of engineering graduate students as they navigate graduate study.