All You Need is Self-Determination: Investigation of PhD Students’ Motivation Profiles and Their Impact on the Doctoral Completion Process

Aim/Purpose: The present study aimed at (1) identifying the naturally occurring patterns of motivation among doctoral students and (2) assessing their impact on the doctoral completion process. Background: Grounded in the self-determination theory, the paper investigated needs satisfaction and the doctoral completion process. Methodology: Two complementary methods were used. First, k-mean clustering was used to classify 461 doctoral students according to their feelings of competence, autonomy and relatedness. Second, the completion process of these five profiles was investigated through multi-group path analyses. Contribution: This paper provided a motivational perspective on doctoral completion process that highlighted significant individual differences. Findings: Five profiles were identified corresponding to different combinations of satisfaction of their innate psychological needs. The results also revealed significant differences in the completion process from one motivation profile to another. Recommendations for Practitioners: The doctoral supervision needs to consider the specificities of the patterns of motivation among doctoral students. Recommendation for Researchers: A more important investigation of motivational patterns is required to fully understand the doctoral completion process. Impact on Society: A better consideration of motivational profiles would increase doctoral students’ well-being and their persistence. Future Research: The effect of motivation and context on student satisfaction and professional efficiency could be further explored.

Response to comment on “Typical physics Ph.D. admissions criteria limit access to underrepresented groups but fail to predict doctoral completion”

We provide statistical measures and additional analyses showing that our original analyses were sound. We use a generalized linear mixed model to account for program-to-program differences with program as a random effect without stratifying with tier and found the GRE-P (Graduate Record Examination physics test) effect is not different from our previous findings, thereby alleviating concern of collider bias. Variance inflation factors for each variable were low, showing that multicollinearity was not a concern. We show that range restriction is not an issue for GRE-P or GRE-V (GRE verbal), and only a minor issue for GRE-Q (GRE quantitative). Last, we use statistical measures of model quality to show that our published models are better than or equivalent to several alternates.

A Qualitative Case Study of All-but-Dissertation Students at Risk for Dissertation Noncompletion: A New Model for Supporting Candidates to Doctoral Completion

This study identified emergent themes from the interview data of at-risk-for-completion doctoral candidates ( N = 13; 59%), from a diverse demographic, who participated in a successful dissertation completion intervention program. The findings revealed four major themes including extrinsic factors, socioemotional, formal structures of the program, and personal development. The findings highlight the need for conscious processes used by vital leaders to develop program design in four key areas of leadership within a framework of open vital systems. Vital leadership acts as proxy agents to influence development of formal structures in the university leading to equity in educational opportunity for all students. Conclusions and parsimonious explicit implications are provided for doctoral program redesign focused on improving graduate student retention and completion rates for diverse student populations.

Typical physics Ph.D. admissions criteria limit access to underrepresented groups but fail to predict doctoral completion

This study aims to understand the effectiveness of typical admissions criteria in identifying students who will complete the Physics Ph.D. Multivariate statistical analysis of roughly one in eight physics Ph.D. students from 2000 to 2010 indicates that the traditional admissions metrics of undergraduate grade point average (GPA) and the Graduate Record Examination (GRE) Quantitative, Verbal, and Physics Subject Tests do not predict completion as effectively admissions committees presume. Significant associations with completion were found for undergraduate GPA in all models and for GRE Quantitative in two of four studied models; GRE Physics and GRE Verbal were not significant in any model. It is notable that completion changed by less than 10% for U.S. physics major test takers scoring in the 10th versus 90th percentile on the Quantitative test. Aside from these limitations in predicting Ph.D. completion overall, overreliance on GRE scores in admissions processes also selects against underrepresented groups.

Dissertation Research Supervisor Agency for U.S. Online Doctoral Research Supervision

This chapter critically explores the construct of agency from a dissertation research supervisor perspective. While the literature has expanded in the exploration of student agency, little focus has been given to the construct from a research supervisor agency stance. Current research into doctoral completion has shown the relationship between supervisor and dissertation writer as critical to persistence and completion. However, less investigation has focused on the aspects of dissertation supervisor agency and the evolution to a high mentoring approach, especially for online doctoral students. The conceptual inquiry utilizes the lens of Lave and Wenger's situated learning theory to view how research supervisor agency can foster and guide doctoral scholars to consider researcher positionality and move from the margins of the doctoral learning community to the center of scholarly life and post-doctoral practice-based research and evidence-based decision making.

The Role of Department Supports on Doctoral Completion and Time-to-Degree

This study examined department supports that influence doctoral completion and time-to-degree among 5,001 doctoral programs at 212 universities in the United States. Multiple regression models were used to determine the relationships between financial support and academic support and doctoral retention and how these relationships vary across STEM and non-STEM fields. Analyzing the American National Research Council’s data, we found that a department’s financial support significantly predict doctoral completion and time-to-degree. However, no overarching relationship was found between department academic support and doctoral completion and time-to-degree. These findings provide a better understanding of how department supports are associated with doctoral student success. The article concludes with implications for theory, practice, and future research.