Denton, M., & Choe, N. H., & Borrego, M. J., & Knight, D. B. (2020, June), Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35014

\bibitem{asee_peer_35014}
  Denton, M., \& Choe, N. H., \& Borrego, M. J., \& Knight, D. B. (2020, June), \emph{Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--35014

Maya Denton, Nathan Hyungsok Choe, Maura J. Borrego, and David B. Knight.  "Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/35014 Internet.  11 Jul, 2024

\bibitem{asee_peer_35014}
  Maya Denton, Nathan Hyungsok Choe, Maura J. Borrego, and David B. Knight.
  "Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/35014 Internet.  11 Jul, 2024

@INPROCEEDINGS{asee_peer_35014
author = "Maya Denton, Nathan Hyungsok Choe, Maura J. Borrego, and David B. Knight"
title = "Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/35014}
number = {10.18260/1-2--35014}
}

TY  - CPAPER
AB  - Our research paper examines the sequence with which engineering doctoral students are funded through different mechanisms, such as research or teaching assistantships or fellowships, which impact their training, experiences, and time to degree. Prior research using the Survey of Earned Doctorates suggests that engineering students funded primarily by research assistantships complete their PhDs on average one semester earlier than students funded primarily by teaching assistantships. However, many doctoral students are funded by multiple sources in various sequences, and national datasets such as Survey of Earned Doctorates do not include semester-by-semester detail about funding nor include students who do not complete a PhD. 
Using a detailed institutional database, we investigated whether and how the timing and type of funding relates to persistence and completion among chemical engineering doctoral students at a large public research institution. Funding mechanisms were categorized as Teaching Assistantship (TA), Research Assistantship (RA), Fellowship, and No University Funding. This program requires all students to serve as a TA prior to degree completion, which raises an interesting policy question of the optimal timing with respect to student degree progress. Our research question is as follows:
How, if at all, does being funded through a TA, RA, or fellowship in a given year predict the 3-, 4-, and 5-year persistence rates and 4-, 5-, and 6-year completion rates of chemical engineering doctoral students?
The data include information on enrollment, degree completion and total dollar amount of funding by month in RA, TA and fellowship categories for all chemical engineering students since 1992. We report count and percentage data related to type of funding for all participants entering from 1992 to 2011. Then, we ran step-wise logistic regression models, which predicted persistence through Years 3, 4, and 5 and completion in Years 4, 5, and 6. Predictors for all logistic regression models included the type of funding in a specific year (e.g., Fellowship 2nd Year or RA 4th Year). Treating funding types as separate variables accounts for the possibility of multiple types of funding for a single participant, with “No University Funding” serving as the reference group in the models. 
Results from the logistic regression models empirically support conventional wisdom about graduate student funding. First, any type of funding is better than no university funding. Having a TA assignment early on or an RA during the middle of graduate school positively predicts student persistence. Second, students supported via RAs and fellowships have stronger persistence and graduation rates than TAs, with the highest, followed by fellowships. Third, completing a TA assignment in later years negatively predicts degree completion.
Although the results are not unexpected, it is important to use empirical evidence to support policy decisions related to allocation of graduate student funding. This research will help graduate program leaders make policy decisions related to allocation of limited funding resources to improve retention and completion rates of all engineering PhD students. 
AU  - Maya Denton
AU  - Nathan Hyungsok Choe
AU  - Maura J. Borrego
AU  - David B. Knight
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Optimal Sequencing of Graduate Funding in a Chemical Engineering Department: Maximizing Completion and Persistence Rates
UR  - https://peer.asee.org/35014
DO  - 10.18260/1-2--35014
ER  -